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[[Machine learning ITI8565]] | [[Machine learning ITI8565]] | ||
| − | + | Spring term 2025 | |
| − | + | ITI8565: Machine learning | |
| − | + | Taught by: Prof. Sven Nõmm | |
| − | + | Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk | |
EAP: 6.0 | EAP: 6.0 | ||
| − | + | Lectures on Tuesdays 12:15-13:45 U06a-209 | |
| − | + | ||
| − | + | Practices on Thursdays 14:00-15:30 ICT-402 | |
| − | + | ||
| − | + | Consultations is by appointment only! Please do not hesitate to ask for consultation! | |
| − | + | ||
| − | + | 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. | |
| + | This page will be populated during the term with lecture with the lecture slides. | ||
| + | |||
| + | |||
| + | =Lectures and tentative time line = | ||
| + | == 04.02.25 Introduction and desistance function == | ||
| + | |||
| + | [[Media:lecture_01_intorduction_and_distance_function_ml_2025_web_version.pdf |Slides]] | ||
| + | |||
| + | == 11.02.25 Cluster Analysis I == | ||
| + | |||
| + | [[Media:lecture_02_cluster_analysis_1_ml_2025.pdf |Slides]] | ||
| + | |||
| + | == 18.02.25 Cluster analysis II == | ||
| + | |||
| + | [[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2025.pdf |Slides]] | ||
| + | |||
| + | [[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2025.pdf |Slides]] | ||
| + | |||
| + | == 25.02.25 Classification I == | ||
| + | |||
| + | [[Media:lecture_04_classification_1_ml_2025.pdf |Slides]] | ||
| − | == | + | == 04.03.25 Regression analysis == |
| + | <span style="color:red"> Deadline to submit first home assignment </span> | ||
| − | + | [[Media:lecture_05_supervised_learning_2_ml_2025.pdf |Slides]] | |
| − | + | ||
| − | + | [[Media:lecture_05_Gradient_descent_andmore_ml_2025.pdf |Slides]] | |
| − | + | ||
| − | + | == 11.03.25 Separability, Support Vector Machines, Kernel Trick == | |
| − | + | ||
| + | [[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2025.pdf |Slides]] | ||
| + | |||
| + | == 18.03.25 Model quality boosting == | ||
| + | |||
| + | [[Media:lecture_07_Model_Quality_Boosting_ML_2025.pdf |Slides]] | ||
| + | |||
| + | == 25.03.25 <span style="color:red"> Closed Book Test I </span> == | ||
| − | = | + | == 01.04.25 Neural networks == |
| − | == | ||
| − | |||
| − | == | + | == 08.04.25 Convolutional Neural Networks == |
| − | |||
| − | + | == 15.04.25 Sequential data modelling == | |
| − | == | + | == 22.04.25 Deep Learning Transformers == |
| − | |||
| − | == | + | == 29.04.25 Generative AI == |
| − | |||
| − | == | + | == 06.05.25 Explainable AI == |
| − | |||
| − | == | + | == 13.05.25 <span style="color:red"> Closed Book Test II </span> == |
| − | |||
| − | == | + | == 20.05.25 TBA == |
| − | |||
| − | + | Grading scale | |
| − | + | *91 < score -- grade 5 (excellent) | |
| − | + | *81 < score < 90 -- grade 4 (very good) | |
| + | *71 < score < 80 -- grade 3 (good) | ||
| + | *61 < score < 70 -- grade 2 (satisfactory) | ||
| + | *51 < score < 60 -- grade 1 (acceptable) | ||
| + | score ≤ 50 -- a student has failed the course | ||
Viimane redaktsioon: 31. jaanuar 2025, kell 12:58
Spring term 2025
ITI8565: Machine learning
Taught by: Prof. Sven Nõmm
Teaching assistants Mr. Jaak Kapten and Mr. Mihhail Daniljuk
EAP: 6.0
Lectures on Tuesdays 12:15-13:45 U06a-209
Practices on Thursdays 14:00-15:30 ICT-402
Consultations is by appointment only! Please do not hesitate to ask for consultation!
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. This page will be populated during the term with lecture with the lecture slides.
Lectures and tentative time line
04.02.25 Introduction and desistance function
11.02.25 Cluster Analysis I
18.02.25 Cluster analysis II
25.02.25 Classification I
04.03.25 Regression analysis
Deadline to submit first home assignment
11.03.25 Separability, Support Vector Machines, Kernel Trick
18.03.25 Model quality boosting
25.03.25 Closed Book Test I
01.04.25 Neural networks
08.04.25 Convolutional Neural Networks
15.04.25 Sequential data modelling
22.04.25 Deep Learning Transformers
29.04.25 Generative AI
06.05.25 Explainable AI
13.05.25 Closed Book Test II
20.05.25 TBA
Grading scale
- 91 < score -- grade 5 (excellent)
- 81 < score < 90 -- grade 4 (very good)
- 71 < score < 80 -- grade 3 (good)
- 61 < score < 70 -- grade 2 (satisfactory)
- 51 < score < 60 -- grade 1 (acceptable)
score ≤ 50 -- a student has failed the course