Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

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[[Machine learning ITI8565]]
 
[[Machine learning ITI8565]]
  
Spring term 2022
+
Spring term 2024
  
 
ITI8565: Machine learning
 
ITI8565: Machine learning
9. rida: 9. rida:
 
EAP: 6.0
 
EAP: 6.0
  
 +
Lectures on Tuesdays 12:00-17:00  ICT-A2
  
Lectures on Tuesdays 13:45-15:15 Online only in MS Teams environment
+
Practices on Thursdays 14:00-15:30 ICT-401
  
Practices on Thursdays 13:40-15:10 Online only in MS Teams environment
+
Consultations is by appointment only!  Please do not hesitate to ask for consultation!
  
Please use code HAL900  to join TalTech Moodle page of the course.
 
 
More information will appear closer to the start of the spring term.
 
  
 
=Lectures =
 
=Lectures =
  
 
== Week 1  Introduction, Distance function ==
 
== Week 1  Introduction, Distance function ==
[[Media:Lecture_1_Intorduction_and_Distance_function_ML_2022.pdf ‎|Slides]]
+
[[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]]
+
[[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]]
+
[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2024.pdf ‎|Slides]]
 +
 
 +
[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2024.pdf ‎|Slides]]
 +
 
 +
== Week 4  Supervised learning I: Classification ==
 +
[[Media:lecture_04_classification_1_ml_2024.pdf ‎|Slides]]
 +
 
 +
== Week 5  Supervised learning II: Regression  ==
 +
[[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]]
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== Week 8  Closed book test 1 ==
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== Week 9  Neural Networks I ==
 +
[[Media: lecture_8_neural_networks_ML_2024.pdf ‎|Slides part I ]]
 +
[[Media: Lecture_8_part_2_neural_networks_ML_2024.pdf ‎|Slides part II]]
 +
[[Media: lecture_08_part_3_neural_networks_2_ML_2024.pdf ‎|Slides part III]]
 +
 
 +
== Week 10  Sequential processes modelling: from Markov Models to LSTM ==
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== Week 11  Deep Learning I: Transformers==
 +
TBA
 +
 
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== Week 12 Deep Learning II: Convolutional neural networks==
 +
TBA
  
[[Media:Lecture_03_2_anomaly_and_otlier_analysis_ML2022.pdf ‎|Slides]]
+
== Week 13 Deep Learning III: Generative AI ==
 +
TBA
  
== Week 4  Supervised learning I ==
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== Week 14 Explainable AI==
[[Media:Lecture_04_Classification_1_ML_2022.pdf ‎|Slides]]
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TBA
  
== Week 5  Supervised learning II ==
 
[[Media:Lecture_05_Supervised_Learning_2_ML_2022.pdf ‎|Slides]].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