Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

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1. rida: 1. rida:
 
[[Machine learning ITI8565]]
 
[[Machine learning ITI8565]]
  
Previous years: [[Machine learning ITI8565 (2017)|2017]]
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Spring term 2024
 
 
Spring 2017/2018
 
  
 
ITI8565: Machine learning
 
ITI8565: Machine learning
11. rida: 9. rida:
 
EAP: 6.0
 
EAP: 6.0
  
Time and place:
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Lectures on Tuesdays 12:00-17:00  ICT-A2
  Lectures: Tuesdays 16:00-17:30  ICT-A1
 
  Labs:  Thursdays  16:00-17:30 ICT-401
 
  Self Practice Fridays 10:00 - 14:00 ICT-405
 
Consultation: TBA
 
 
Additional information: sven.nomm@ttu.ee
 
  
==Evaluation==
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Practices on Thursdays 14:00-15:30  ICT-401
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Consultations is by appointment only!  Please do not hesitate to ask for consultation!
  
*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
 
  
 
=Lectures =
 
=Lectures =
== Lecture 1  Introduction and distance function ==
 
[[Media:Lecture_1_Intorduction_and_DistanceFunction_ML_2018.pdf ‎|Slides]]
 
  
== Lecture 2  Cluster Analysis I ==
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== Week 1  Introduction, Distance function ==
[[Media:Lecture_2_Cluster_Analysis_1_ML_2018.pdf ‎|Slides]]
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[[Media:lecture_01_intorduction_and_distance_function_ml_2024_web_version.pdf ‎|Slides]]
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== Week 2  Cluster analysis I ==
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[[Media:lecture_02_cluster_analysis_1_ml_2024.pdf ‎|Slides]]
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== Week 3  Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) ==
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[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2024.pdf ‎|Slides]]
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[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2024.pdf ‎|Slides]]
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== Week 4  Supervised learning I: Classification ==
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[[Media:lecture_04_classification_1_ml_2024.pdf ‎|Slides]]
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== Week 5  Supervised learning II: Regression  ==
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[[Media:lecture_05_supervised_learning_2_ml_2024.pdf ‎|Slides]]
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== Week 6  Supervised learning III: Gradient descent ==
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[[Media:lecture_06_Gradient_descent_andmore_ml_2024.pdf ‎|Slides]]
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[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2024.pdf ‎|Slides]]
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== Week 7  Supervised learning V: Model quality boosting ==
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[[Media:lecture_07_Model_Quality_Boosting_ML_2024.pdf ‎|Slides]]
  
[[Media:Data_sets.zip ‎|Data sets for practice]]
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== Week 8  Closed book test 1 ==
  
== Lecture 3 Cluster Analysis II Probabilistic approach ==
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== Week 9 Neural Networks I ==
[[Media:Lecture_3_Cluster_Analysis_2_ML_2018.pdf ‎|Slides]]
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[[Media: lecture_8_neural_networks_ML_2024.pdf ‎|Slides part I ]]
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[[Media: Lecture_8_part_2_neural_networks_ML_2024.pdf ‎|Slides part II]]
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[[Media: lecture_08_part_3_neural_networks_2_ML_2024.pdf ‎|Slides part III]]
  
== Lecture 4  Supervised Learning I ==
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== Week 10 Sequential processes modelling: from Markov Models to LSTM ==
[[Media:Lecture_4_Classification_1_ML_2018.pdf ‎|Slides]]
 
  
== Lecture 5  Supervised Learning II  ==
 
[[Media:Lecture_5_Classification_2_ML_2018.pdf ‎|Slides]]
 
  
== Lecture 6 Supervised Learning III  ==
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== Week 11 Deep Learning I: Transformers==
[[Media:Lecture_6_Gradient_descent_andmore_ML_2018.pdf ‎|Slides]]
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TBA
  
== Lecture 7  Supervised Learning IV  ==
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== Week 12 Deep Learning II: Convolutional neural networks==
[[Media:Lecture_7_Support_Vector_Machines_Kernel_Trick_ML_2018.pdf ‎|Slides]]
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TBA
  
== Lecture 9  Supervised Learning VI: Neural Networks  ==
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== Week 13 Deep Learning III: Generative AI ==
[[Media:Lecture_9_Neural_Networks_ML_2018.pdf ‎|Slides]]
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TBA
[[Media:Lecture_9_part2_Neural_Networks_ML_2018.pdf ‎|Slides]]
 
  
== Lecture 10  Supervised Learning VII: Neural Networks  ==
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== Week 14 Explainable AI==
[[Media:Lecture_10_Neural_Networks_ML_2018.pdf ‎|Slides]]
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TBA
  
== Lecture 11  Guest lecture on cybersecurity by Hayretdin Bahsi==
 
[[Media:Cyber_Security_Guest_Lecture.pdf ‎|Slides]]
 
  
== Lecture 12  Enseble learning==
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[[Media:Lecture_11_Bagging_and_Ensembles_2018.pdf ‎|Slides]]
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 +
*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: 25. märts 2024, kell 11:32

Machine learning ITI8565

Spring term 2024

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0

Lectures on Tuesdays 12:00-17:00 ICT-A2

Practices on Thursdays 14:00-15:30 ICT-401

Consultations is by appointment only! Please do not hesitate to ask for consultation!


Lectures

Week 1 Introduction, Distance function

Slides

Week 2 Cluster analysis I

Slides

Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis)

Slides

Slides

Week 4 Supervised learning I: Classification

Slides

Week 5 Supervised learning II: Regression

Slides

Week 6 Supervised learning III: Gradient descent

Slides

Slides

Week 7 Supervised learning V: Model quality boosting

Slides

Week 8 Closed book test 1

Week 9 Neural Networks I

Slides part I Slides part II Slides part III

Week 10 Sequential processes modelling: from Markov Models to LSTM

Week 11 Deep Learning I: Transformers

TBA

Week 12 Deep Learning II: Convolutional neural networks

TBA

Week 13 Deep Learning III: Generative AI

TBA

Week 14 Explainable AI

TBA



  • 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