Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

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[[Machine learning ITI8565]]
 
[[Machine learning ITI8565]]
  
Spring term 2022
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Spring term 2024
  
 
ITI8565: Machine learning
 
ITI8565: Machine learning
9. rida: 9. rida:
 
EAP: 6.0
 
EAP: 6.0
  
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Lectures on Tuesdays 12:00-17:00  ICT-A2
The course will continue purely in online mode!!!
 
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Lectures on Tuesdays 13:45-15:15 Online only in MS Teams environment
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Practices on Thursdays 14:00-15:30 ICT-401
  
Practices on Thursdays 13:40-15:10 Online only in MS Teams environment
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Consultations is by appointment onlyPlease do not hesitate to ask for consultation!
 
 
Please use code HAL900  to join TalTech Moodle page of the course.
 
  
  
23. rida: 19. rida:
  
 
== Week 1  Introduction, Distance function ==
 
== Week 1  Introduction, Distance function ==
[[Media:Lecture_1_Intorduction_and_Distance_function_ML_2022.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_2022.pdf ‎|Slides]]
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[[Media:lecture_02_cluster_analysis_1_ml_2024.pdf ‎|Slides]]
  
 
== Week 3  Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) ==
 
== Week 3  Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) ==
[[Media:Lecture_03_1_Cluster_Analysis_2_Probabilistic_approachML_2022.pdf ‎|Slides]]
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[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2024.pdf ‎|Slides]]
  
[[Media:Lecture_03_2_anomaly_and_otlier_analysis_ML2022.pdf ‎|Slides]]
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[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2024.pdf ‎|Slides]]
  
== Week 4  Supervised learning I: Feature selection kNN and regression ==
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== Week 4  Supervised learning I: Classification ==
[[Media:Lecture_04_Classification_1_ML_2022.pdf ‎|Slides]]
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[[Media:lecture_04_classification_1_ml_2024.pdf ‎|Slides]]
  
== Week 5  Supervised learning II: Regression and decision trees ==
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== Week 5  Supervised learning II: Regression ==
[[Media:Lecture_05_Supervised_Learning_2_ML_2022.pdf ‎|Slides]]
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[[Media:lecture_05_supervised_learning_2_ml_2024.pdf ‎|Slides]]
  
 
== Week 6  Supervised learning III: Gradient descent ==
 
== Week 6  Supervised learning III: Gradient descent ==
[[Media:Lecture_6_Gradient_descent_andmore_ML_2022.pdf ‎|Slides]]
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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]]
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== Week 8  Closed book test 1 ==
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== Week 9  Neural Networks I ==
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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]]
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== Week 10  Sequential processes modelling: from Markov Models to LSTM ==
  
== Week 7  Supervised learning IV: Support Vector Machine ==
 
[[Media:Lecture_07_Support_Vector_Machines_Kernel_Trick_ML_2022.pdf ‎|Slides]]
 
  
== Week 9 Supervised learning V: Model quality boosting ==
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== Week 11 Deep Learning I: Transformers==
[[Media:Lecture_09_Model_Quality_Boosting_ML_2022.pdf ‎|Slides]]
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TBA
  
== Week 10  Supervised learning VI: Model quality boosting ==
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== Week 12 Deep Learning II: Convolutional neural networks==
[[Media:Markov model (1) (1).pdf ‎|Slides]]
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TBA
  
== Week 11  Neural Networks I ==
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== Week 13 Deep Learning III: Generative AI ==
[[Media: Lecture_11_Neural_Networks_ML_2022.pdf ‎|Slides]]
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TBA
  
== Week 12  Neural Networks II ==
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== Week 14 Explainable AI==
[[Media:Lecture_12_Neural_Networks_ML_2022.pdf ‎|Slides]]
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TBA
  
== Week 13  Neural Networks III ==
 
[[Media:Lecture_13_Neural_Network_Intuition_ML_2022.pdf ‎|Slides]]
 
  
== Week 14  Neural Networks IV ==
 
[[Media:Lecture_14_Deep_Learning_CNN_ML_2022.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