http://courses.cs.taltech.ee/w/api.php?action=feedcontributions&feedformat=atom&user=SvenKursused - Kasutaja kaastöö [et]2026-05-21T20:01:12ZKasutaja kaastööMediaWiki 1.35.9http://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11685Machine learning ITI85652025-01-31T12:58:56Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
[[Media:lecture_02_cluster_analysis_1_ml_2025.pdf |Slides]]<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2025.pdf |Slides]]<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
[[Media:lecture_04_classification_1_ml_2025.pdf |Slides]]<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
[[Media:lecture_05_supervised_learning_2_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_05_Gradient_descent_andmore_ml_2025.pdf |Slides]]<br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2025.pdf |Slides]]<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
[[Media:lecture_07_Model_Quality_Boosting_ML_2025.pdf |Slides]]<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_07_Model_Quality_Boosting_ML_2025.pdf&diff=11684Fail:Lecture 07 Model Quality Boosting ML 2025.pdf2025-01-31T12:37:12Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2025.pdf&diff=11683Fail:Lecture 06 Support Vector Machines Kernel Trick ML 2025.pdf2025-01-31T12:36:48Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_05_Gradient_descent_andmore_ml_2025.pdf&diff=11682Fail:Lecture 05 Gradient descent andmore ml 2025.pdf2025-01-31T12:36:06Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_05_supervised_learning_2_ml_2025.pdf&diff=11681Fail:Lecture 05 supervised learning 2 ml 2025.pdf2025-01-31T12:35:51Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_04_classification_1_ml_2025.pdf&diff=11680Fail:Lecture 04 classification 1 ml 2025.pdf2025-01-31T12:35:01Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11679Machine learning ITI85652025-01-31T12:33:56Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
[[Media:lecture_02_cluster_analysis_1_ml_2025.pdf |Slides]]<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2025.pdf |Slides]]<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
[[Media:lecture_04_classification_1_ml_2025.pdf |Slides]]<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
[[Media:lecture_05_supervised_learning_2_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_05_Gradient_descent_andmore_ml_2025.pdf |Slides]]<br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2025.pdf |Slides]]<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
[[Media:lecture_07_Model_Quality_Boosting_ML_2025.pdf |Slides]]<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11678Machine learning ITI85652025-01-31T12:32:12Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
[[Media:lecture_02_cluster_analysis_1_ml_2025.pdf |Slides]]<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2025.pdf |Slides]]<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
[[Media:lecture_04_classification_1_ml_2025.pdf |Slides]]<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
[[Media:lecture_05_supervised_learning_2_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_05_Gradient_descent_andmore_ml_2025.pdf |Slides]]<br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11677Machine learning ITI85652025-01-31T12:30:44Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
[[Media:lecture_02_cluster_analysis_1_ml_2025.pdf |Slides]]<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2025.pdf |Slides]]<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
[[Media:lecture_04_classification_1_ml_2025.pdf |Slides]]<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_03_2_anomaly_and_otlier_analysis_ml_2025.pdf&diff=11676Fail:Lecture 03 2 anomaly and otlier analysis ml 2025.pdf2025-01-31T12:29:39Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2025.pdf&diff=11675Fail:Lecture 03 1 cluster analysis 2 probabilistic approach ml 2025.pdf2025-01-31T12:29:27Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11674Machine learning ITI85652025-01-31T12:29:12Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
[[Media:lecture_02_cluster_analysis_1_ml_2025.pdf |Slides]]<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2025.pdf |Slides]]<br />
<br />
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2025.pdf |Slides]]<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_02_cluster_analysis_1_ml_2025.pdf&diff=11673Fail:Lecture 02 cluster analysis 1 ml 2025.pdf2025-01-31T12:27:53Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11672Machine learning ITI85652025-01-31T12:27:41Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
[[Media:lecture_02_cluster_analysis_1_ml_2025.pdf |Slides]]<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf&diff=11671Fail:Lecture 01 intorduction and distance function ml 2025 web version.pdf2025-01-31T12:26:53Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11670Machine learning ITI85652025-01-31T12:26:37Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11669Machine learning ITI85652025-01-31T11:12:33Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
=Lectures and tentative time line =<br />
== 04.02.25 Introduction and desistance function ==<br />
<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 11.02.25 Cluster Analysis I ==<br />
<br />
== 18.02.25 Cluster analysis II ==<br />
<br />
== 25.02.25 Classification I ==<br />
<br />
== 04.03.25 Regression analysis == <br />
<span style="color:red"> Deadline to submit first home assignment </span><br />
<br />
== 11.03.25 Separability, Support Vector Machines, Kernel Trick ==<br />
<br />
== 18.03.25 Model quality boosting ==<br />
<br />
== 25.03.25 <span style="color:red"> Closed Book Test I </span> ==<br />
<br />
== 01.04.25 Neural networks ==<br />
<br />
== 08.04.25 Convolutional Neural Networks ==<br />
<br />
== 15.04.25 Sequential data modelling == <br />
<br />
== 22.04.25 Deep Learning Transformers ==<br />
<br />
== 29.04.25 Generative AI ==<br />
<br />
== 06.05.25 Explainable AI ==<br />
<br />
== 13.05.25 <span style="color:red"> Closed Book Test II </span> ==<br />
<br />
== 20.05.25 TBA ==<br />
<br />
Grading scale<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11668Machine learning ITI85652025-01-29T14:11:28Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2025<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Prof. Sven Nõmm<br />
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:15-13:45 U06a-209<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-402<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
Please refer to TalTech Moodle page of the course and MS Teams team of the course for up to date slides and files necessary for practice sessions. <br />
<br />
=Lectures =<br />
<br />
This page will be populated during the term with lecture with the lecture slides. <br />
<br />
<br />
<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_09_DM2024_Similarity_and_Distance_2.pdf&diff=11570Fail:Lecture 09 DM2024 Similarity and Distance 2.pdf2024-11-04T15:55:26Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11569Data Mining (ITI8730)2024-11-04T15:55:11Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 01.10.24 Cluster analysis I==<br />
[[Media:Lecture_05_DM2024_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 08.10.24 Association pattern mining ==<br />
[[Media:Lecture_06_DM2024_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 15.10.24 Clustering II ==<br />
[[Media:Lecture_07_DM_2024_Cluster_analysis_EM_algorithm.pdf |Slides]]<br />
<br />
== 22.10.24 Anomaly and Outlier Analysis ==<br />
[[Media:Lecture_08_DM2024_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
<br />
== 05.11.24 Similarity and Distance II ==<br />
[[Media:Lecture_09_DM2024_Similarity_and_Distance_2.pdf |Slides]]</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_08_DM2024_Anomaly_and_Outlier_Analysis.pdf&diff=11550Fail:Lecture 08 DM2024 Anomaly and Outlier Analysis.pdf2024-10-21T10:02:36Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11549Data Mining (ITI8730)2024-10-21T10:02:09Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 01.10.24 Cluster analysis I==<br />
[[Media:Lecture_05_DM2024_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 08.10.24 Association pattern mining ==<br />
[[Media:Lecture_06_DM2024_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 15.10.24 Clustering II ==<br />
[[Media:Lecture_07_DM_2024_Cluster_analysis_EM_algorithm.pdf |Slides]]<br />
<br />
== 22.10.24 Anomaly and Outlier Analysis ==<br />
[[Media:Lecture_08_DM2024_Anomaly_and_Outlier_Analysis.pdf |Slides]]</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_07_DM_2024_Cluster_analysis_EM_algorithm.pdf&diff=11547Fail:Lecture 07 DM 2024 Cluster analysis EM algorithm.pdf2024-10-14T10:52:54Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11546Data Mining (ITI8730)2024-10-14T10:52:39Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 01.10.24 Cluster analysis I==<br />
[[Media:Lecture_05_DM2024_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 08.10.24 Association pattern mining ==<br />
[[Media:Lecture_06_DM2024_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 15.10.24 Clustering II ==<br />
[[Media:Lecture_07_DM_2024_Cluster_analysis_EM_algorithm.pdf |Slides]]</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_06_DM2024_Association_Pattern_Mining.pdf&diff=11540Fail:Lecture 06 DM2024 Association Pattern Mining.pdf2024-10-08T07:41:06Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11539Data Mining (ITI8730)2024-10-08T07:40:51Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 01.10.24 Cluster analysis I==<br />
[[Media:Lecture_05_DM2024_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 08.10.24 Association pattern mining ==<br />
[[Media:Lecture_06_DM2024_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11529Data Mining (ITI8730)2024-09-30T20:43:54Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 01.10.24 ==<br />
[[Media:Lecture_05_DM2024_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_05_DM2024_Cluster_analysis_I.pdf&diff=11528Fail:Lecture 05 DM2024 Cluster analysis I.pdf2024-09-30T20:42:54Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11527Data Mining (ITI8730)2024-09-30T20:42:07Z<p>Sven: /* 01.10.24 */</p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 01.10.24 ==<br />
[[MediaLecture_05_DM2024_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11526Data Mining (ITI8730)2024-09-30T20:41:34Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 01.10.24 ==<br />
[[MediaLecture_05_DM2024_Cluster_analysis_I.pdf |Slides]]<br />
<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf&diff=11521Fail:Lecture 04 DM2024 Regression analysis and data preparation.pdf2024-09-23T20:09:17Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11520Data Mining (ITI8730)2024-09-23T20:08:04Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 24.09.24 ==<br />
[[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_03_Classification_II_DM_2024.pdf&diff=11506Fail:Lecture 03 Classification II DM 2024.pdf2024-09-16T15:03:45Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11505Data Mining (ITI8730)2024-09-16T15:03:31Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
<br />
== 17.09.24 Classification II ==<br />
[[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_02_DM2024_Classification_I.pdf&diff=11497Fail:Lecture 02 DM2024 Classification I.pdf2024-09-10T06:20:32Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11496Data Mining (ITI8730)2024-09-10T06:20:18Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 10.09.24 Classification I ==<br />
[[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11487Data Mining (ITI8730)2024-09-05T09:08:38Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-121<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11485Data Mining (ITI8730)2024-09-02T21:04:00Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_01_DM2024_Introduction_distance_functions.pdf&diff=11476Fail:Lecture 01 DM2024 Introduction distance functions.pdf2024-09-02T09:39:16Z<p>Sven: Sven laadis üles uue versiooni failist Fail:Lecture 01 DM2024 Introduction distance functions.pdf</p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Fail:Lecture_01_DM2024_Introduction_distance_functions.pdf&diff=11475Fail:Lecture 01 DM2024 Introduction distance functions.pdf2024-09-02T09:19:20Z<p>Sven: </p>
<hr />
<div></div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11474Data Mining (ITI8730)2024-09-02T09:19:01Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICT-702<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 03.09.24 Distance function ==<br />
[[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11473Data Mining (ITI8730)2024-08-30T12:00:07Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICT-702<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Classification<br />
* Clustering<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Classification, Cluster Analysis, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 05.09.23 Distance function ==<br />
[[Media:Lecture_01_DM2023_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11472Data Mining (ITI8730)2024-08-30T11:58:04Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICT-702<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Clustering<br />
* Classification<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Cluster Analysis, Classification, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 05.09.23 Distance function ==<br />
[[Media:Lecture_01_DM2023_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11471Data Mining (ITI8730)2024-08-30T11:56:16Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICT-702<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Clustering<br />
* Classification<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Cluster Analysis, Classification, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
<br />
=Lectures and Time line =<br />
== 05.09.23 Distance function ==<br />
[[Media:Lecture_01_DM2023_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11465Data Mining (ITI8730)2024-08-22T12:14:02Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants Mihhail Daniljuk, Rajesh Kalakoti<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICT-702<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Clustering<br />
* Classification<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Cluster Analysis, Classification, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
Please note below are the slides from previous year. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. <br />
=Lectures and Time line =<br />
== 05.09.23 Distance function ==<br />
[[Media:Lecture_01_DM2023_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11464Data Mining (ITI8730)2024-08-22T12:10:36Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Prof. Sven Nõmm<br />
<br />
Teaching assistants Mihhail Daniljuk, Rajesh Kalakoti<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICT-702<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Clustering<br />
* Classification<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Cluster Analysis, Classification, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
<br />
=Lectures and Time line =<br />
== 05.09.23 Distance function ==<br />
[[Media:Lecture_01_DM2023_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Data_Mining_(ITI8730)&diff=11463Data Mining (ITI8730)2024-08-22T12:10:19Z<p>Sven: </p>
<hr />
<div><br />
<span style="color:red"> Information for perspective students:</span><br />
<br />
<span style="color:red"> Lecture schedule and slides content are tentative. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span><br />
<br />
<span style="color:red"> The course is open to students with valid TalTech UniID!<br />
The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. <br />
</span><br />
<br />
<span style="color:red"><br />
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. <br />
</span><br />
<br />
<span style="color:red"><br />
Those planning to use their own computers please install "R" and "R-studio".<br />
</span><br />
<br />
<br />
Fall 2024<br />
<br />
ITI8730: Data Mining and network analysis<br />
<br />
Old code for this course is IDN0110<br />
<br />
Taught by: Sven Nõmm<br />
<br />
Teaching assistants Mihhail Daniljuk, Rajesh Kalakoti<br />
<br />
EAP: 6.0<br />
<br />
Lectures: Tuesdays 12:00 - 13:30 ICT-702<br />
<br />
Labs (practices): Thursdays 14:00 - 15:30 ICT-403<br />
<br />
Link to join MS Teams <br />
<br />
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!!<br />
For communication please use the following e-mail: sven.nomm@taltech.ee<br />
<br />
==Prerequisites to join the course ==<br />
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language. <br />
<br />
==Overview ==<br />
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:<br />
* Clustering<br />
* Classification<br />
* Association pattern mining<br />
* Outlier analysis<br />
<br />
Main topics of the course:<br />
* Data types and Data Preparation<br />
* Similarity and Distances, Association Pattern Mining,<br />
* Cluster Analysis, Classification, Outlier analysis<br />
* Data streams, Text Data, Time Series, Discrete Sequences,<br />
* Graph Data, Social Network Analysis<br />
<br />
==Evaluation==<br />
*2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.<br />
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.<br />
*final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.<br />
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).<br />
<br />
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.<br />
<br />
=Lectures and Time line =<br />
== 05.09.23 Distance function ==<br />
[[Media:Lecture_01_DM2023_Introduction_distance_functions.pdf |Slides]]<br />
<br />
== 12.09.23 Cluster analysis I ==<br />
[[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]]<br />
<br />
== 19.09.23 Cluster analysis II ==<br />
[[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]]<br />
<br />
[[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]]<br />
<br />
== 26.09.23 Anomaly and outlier analysis ==<br />
[[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]]<br />
<br />
== 03.10.23 Classification I ==<br />
[[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]]<br />
<br />
== 10.10.23 Classification II ==<br />
[[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]]<br />
<br />
== 17.10.23 Regression analysis ==<br />
[[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]]<br />
<br />
== 24.10.23 Association Pattern mining ==<br />
[[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]]<br />
<br />
== 31.10.23 Closed Book Test I ==<br />
<br />
== 07.11.23 Distance and Similarity II ==<br />
[[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]]<br />
<br />
== 14.11.23 Mining the Time series ==<br />
[[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]]<br />
<br />
== 21.11.23 Mining data streams ==<br />
[[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]]<br />
<br />
== 28.11.23 Text data mining ==<br />
[[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]]<br />
<br />
== 05.12.23 Graph data mining and Social analysis ==<br />
[[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]]<br />
<br />
[[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]]<br />
<br />
== 12.12.23 Privacy preserving data mining==<br />
[[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]]<br />
<br />
== 19.12.23 Closed Book Test II ==</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11358Machine learning ITI85652024-03-25T11:32:14Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2024<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Sven Nõmm<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:00-17:00 ICT-A2<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-401<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
<br />
=Lectures =<br />
<br />
== Week 1 Introduction, Distance function ==<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2024_web_version.pdf |Slides]]<br />
<br />
== Week 2 Cluster analysis I ==<br />
[[Media:lecture_02_cluster_analysis_1_ml_2024.pdf |Slides]]<br />
<br />
== Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) ==<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2024.pdf |Slides]]<br />
<br />
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2024.pdf |Slides]]<br />
<br />
== Week 4 Supervised learning I: Classification ==<br />
[[Media:lecture_04_classification_1_ml_2024.pdf |Slides]]<br />
<br />
== Week 5 Supervised learning II: Regression ==<br />
[[Media:lecture_05_supervised_learning_2_ml_2024.pdf |Slides]]<br />
<br />
== Week 6 Supervised learning III: Gradient descent ==<br />
[[Media:lecture_06_Gradient_descent_andmore_ml_2024.pdf |Slides]]<br />
<br />
[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2024.pdf |Slides]]<br />
<br />
== Week 7 Supervised learning V: Model quality boosting ==<br />
[[Media:lecture_07_Model_Quality_Boosting_ML_2024.pdf |Slides]]<br />
<br />
== Week 8 Closed book test 1 ==<br />
<br />
== Week 9 Neural Networks I ==<br />
[[Media: lecture_8_neural_networks_ML_2024.pdf |Slides part I ]]<br />
[[Media: Lecture_8_part_2_neural_networks_ML_2024.pdf |Slides part II]]<br />
[[Media: lecture_08_part_3_neural_networks_2_ML_2024.pdf |Slides part III]]<br />
<br />
== Week 10 Sequential processes modelling: from Markov Models to LSTM ==<br />
<br />
<br />
== Week 11 Deep Learning I: Transformers==<br />
TBA<br />
<br />
== Week 12 Deep Learning II: Convolutional neural networks==<br />
TBA<br />
<br />
== Week 13 Deep Learning III: Generative AI ==<br />
TBA<br />
<br />
== Week 14 Explainable AI==<br />
TBA<br />
<br />
<br />
<br />
<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11357Machine learning ITI85652024-03-25T11:29:52Z<p>Sven: </p>
<hr />
<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2024<br />
<br />
ITI8565: Machine learning<br />
<br />
Taught by: Sven Nõmm<br />
<br />
EAP: 6.0<br />
<br />
Lectures on Tuesdays 12:00-17:00 ICT-A2<br />
<br />
Practices on Thursdays 14:00-15:30 ICT-401<br />
<br />
Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
<br />
<br />
=Lectures =<br />
<br />
== Week 1 Introduction, Distance function ==<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2024_web_version.pdf |Slides]]<br />
<br />
== Week 2 Cluster analysis I ==<br />
[[Media:lecture_02_cluster_analysis_1_ml_2024.pdf |Slides]]<br />
<br />
== Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) ==<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2024.pdf |Slides]]<br />
<br />
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2024.pdf |Slides]]<br />
<br />
== Week 4 Supervised learning I: Classification ==<br />
[[Media:lecture_04_classification_1_ml_2024.pdf |Slides]]<br />
<br />
== Week 5 Supervised learning II: Regression ==<br />
[[Media:lecture_05_supervised_learning_2_ml_2024.pdf |Slides]]<br />
<br />
== Week 6 Supervised learning III: Gradient descent ==<br />
[[Media:lecture_06_Gradient_descent_andmore_ml_2024.pdf |Slides]]<br />
<br />
[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2024.pdf |Slides]]<br />
<br />
== Week 7 Supervised learning V: Model quality boosting ==<br />
[[Media:lecture_07_Model_Quality_Boosting_ML_2024.pdf |Slides]]<br />
<br />
== Week 8 Closed book test 1 ==<br />
<br />
== Week 9 Neural Networks I ==<br />
[[Media: lecture_8_neural_networks_ML_2024.pdf |Slides part I ]]<br />
[[Media: Lecture_8_part_2_neural_networks_ML_2024.pdf |Slides part II]]<br />
[[Media: lecture_08_part_3_neural_networks_2_ML_2024.pdf |Slides part III]]<br />
<br />
== Week 10 Sequential processes modelling: from Markov Models to LSTM ==<br />
<br />
<br />
== Week 11 Deep Learning I: Transformers==<br />
TBA<br />
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== Week 12 Deep Learning II: Convolutional neural networks==<br />
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== Week 13 Deep Learning III: Generative AI ==<br />
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== Week 14 Explainable AI==<br />
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*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Svenhttp://courses.cs.taltech.ee/w/index.php?title=Machine_learning_ITI8565&diff=11356Machine learning ITI85652024-03-25T11:29:25Z<p>Sven: </p>
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<div>[[Machine learning ITI8565]]<br />
<br />
Spring term 2024<br />
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ITI8565: Machine learning<br />
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Taught by: Sven Nõmm<br />
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EAP: 6.0<br />
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Lectures on Tuesdays 12:00-17:00 ICT-A2<br />
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Practices on Thursdays 14:00-15:30 ICT-401<br />
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Consultations is by appointment only! Please do not hesitate to ask for consultation! <br />
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<pre style="color: red"><br />
Some slides below are mostly from the year 2023. You are welcome to use this material as the reference but be aware that this year the course content will be revised and a few news topics will be added. <br />
</pre><br />
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=Lectures =<br />
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== Week 1 Introduction, Distance function ==<br />
[[Media:lecture_01_intorduction_and_distance_function_ml_2024_web_version.pdf |Slides]]<br />
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== Week 2 Cluster analysis I ==<br />
[[Media:lecture_02_cluster_analysis_1_ml_2024.pdf |Slides]]<br />
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== Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) ==<br />
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2024.pdf |Slides]]<br />
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[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2024.pdf |Slides]]<br />
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== Week 4 Supervised learning I: Classification ==<br />
[[Media:lecture_04_classification_1_ml_2024.pdf |Slides]]<br />
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== Week 5 Supervised learning II: Regression ==<br />
[[Media:lecture_05_supervised_learning_2_ml_2024.pdf |Slides]]<br />
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== Week 6 Supervised learning III: Gradient descent ==<br />
[[Media:lecture_06_Gradient_descent_andmore_ml_2024.pdf |Slides]]<br />
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[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2024.pdf |Slides]]<br />
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== Week 7 Supervised learning V: Model quality boosting ==<br />
[[Media:lecture_07_Model_Quality_Boosting_ML_2024.pdf |Slides]]<br />
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== Week 8 Closed book test 1 ==<br />
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== Week 9 Neural Networks I ==<br />
[[Media: lecture_8_neural_networks_ML_2024.pdf |Slides part I ]]<br />
[[Media: Lecture_8_part_2_neural_networks_ML_2024.pdf |Slides part II]]<br />
[[Media: lecture_08_part_3_neural_networks_2_ML_2024.pdf |Slides part III]]<br />
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== Week 10 Sequential processes modelling: from Markov Models to LSTM ==<br />
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== Week 11 Deep Learning I: Transformers==<br />
TBA<br />
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== Week 12 Deep Learning II: Convolutional neural networks==<br />
<br />
<br />
== Week 13 Deep Learning III: Generative AI ==<br />
<br />
<br />
== Week 14 Explainable AI==<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
*91 < score -- grade 5 (excellent)<br />
*81 < score < 90 -- grade 4 (very good)<br />
*71 < score < 80 -- grade 3 (good)<br />
*61 < score < 70 -- grade 2 (satisfactory)<br />
*51 < score < 60 -- grade 1 (acceptable)<br />
score ≤ 50 -- a student has failed the course</div>Sven