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[[Machine learning ITI8565]]
 
[[Machine learning ITI8565]]
  
Spring term 2021
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Spring term 2024
  
 
ITI8565: Machine learning
 
ITI8565: Machine learning
9. rida: 9. rida:
 
EAP: 6.0
 
EAP: 6.0
  
NB! At least in the beginning of the spring term all teaching will be conducted online.
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Lectures on Tuesdays 12:00-17:00 ICT-A2
Please joint MS Teams! Team name Machine learning ITI8565; Spring term 2021  The code to join the team is '''gkq6q3q'''
 
It is recommended to download and install MS Teams as standalone application and login there with TalTech UniID account.  
 
  
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Practices on Thursdays 14:00-15:30  ICT-401
  
Lectures on Tuesdays 14:00 - 15:30 Online in MS Teams
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Consultations is by appointment only! Please do not hesitate to ask for consultation!
 
 
Practices on Thursdays 17:45 - 19:15  Online in MS Teams
 
  
  
 
=Lectures =
 
=Lectures =
  
== Week 1  Distance function ==
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== Week 1  Introduction, Distance function ==
[[Media:Lecture_1_Intorduction_and_DistanceFunction_ML_2021.pdf ‎|Slides]]
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[[Media:lecture_01_intorduction_and_distance_function_ml_2024_web_version.pdf ‎|Slides]]
  
 
== Week 2  Cluster analysis I ==
 
== Week 2  Cluster analysis I ==
[[Media:Lecture_02_Cluster_Analysis_1_ML_2021.pdf ‎|Slides]]
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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]]
  
== Week 3 Cluster analysis II ==
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== Week 5 Supervised learning II: Regression  ==
[[Media:Lecture_03_Cluster_Analysis_2_Probabilistic_approachML_2021.pdf ‎|Slides]]
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[[Media:lecture_05_supervised_learning_2_ml_2024.pdf ‎|Slides]]
  
== Week 3 Anomaly and outlier analysis ==
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== Week 6 Supervised learning III: Gradient descent ==
[[Media:Lecture_04_anomaly_and_otlier_analysis_ML2021.pdf ‎|Slides]]
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[[Media:lecture_06_Gradient_descent_andmore_ml_2024.pdf ‎|Slides]]
  
== Week 4  Supervised Learning I ==
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[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2024.pdf ‎|Slides]]
[[Media:Lecture_05_Classification_1_ML_2021.pdf ‎|Slides]]
 
  
== Week 5 Supervised Learning II ==
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== Week 7 Supervised learning V: Model quality boosting ==
[[Media:Lecture_06_Classification_2_ML_2021.pdf ‎|Slides]]
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[[Media:lecture_07_Model_Quality_Boosting_ML_2024.pdf ‎|Slides]]
  
NB! Home assignment 1 will be distributed during the practice on 25.02.2021
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== Week 8  Closed book test 1 ==
  
== Week 6 Supervised Learning III ==
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== Week 9 Neural Networks I ==
[[Media:Lecture_7_Gradient_descent_andmore_ML_2021.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]]
  
== Week 7 ==
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== Week 10 Sequential processes modelling: from Markov Models to LSTM ==
  
Closed book test on 16.03.2021
 
  
Defense of Home assignment I on 18.03.2021
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== Week 11  Deep Learning I: Transformers==
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TBA
  
== Week 8  Supervised Learning IV ==
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== Week 12 Deep Learning II: Convolutional neural networks==
[[Media:Lecture_8_Supervised_learning_IV_NaiveBayes_ML_2021.pdf ‎|Slides]]
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TBA
  
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== Week 13 Deep Learning III: Generative AI ==
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TBA
  
== Week 9  Supervised Learning IV ==
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== Week 14 Explainable AI==
[[Media:Lecture_9_Support_Vector_Machines_Kernel_Trick_ML_2021.pdf ‎|Slides]]
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TBA
  
== Week 10  Supervised Learning V; Neural Networks ==
 
[[Media:Lecture_10_Neural_Networks_ML_2021.pdf ‎|Slides]]
 
  
[[Media:Lecture_10_part2_Neural_Networks_ML_2021.pdf ‎|Slides]]
 
  
  

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