Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

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1. rida: 1. rida:
 
[[Machine learning ITI8565]]
 
[[Machine learning ITI8565]]
  
Spring term 2020
+
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
  
Time and place:
+
Practices on Thursdays 14:00-15:30  ICT-401
  Lectures: Thursdays 14:15-15:45  ICT-315
 
  Labs:  Thursdays  16:00-17:30  ICT-401
 
  
Consultation: By appointment. Please do not hesitate to ask for an appointment.
+
Consultations is by appointment only!  Please do not hesitate to ask for consultation!
 
Additional information: sven.nomm@taltech.ee
 
  
Lecture slides will appear here each week AFTER the lecture!
 
Moodle environment @ ained.ttu.ee will be available for registration on  30.01.2020  16:00.
 
  
 
=Lectures =
 
=Lectures =
== Lecture 1  Introduction ==
 
[[Media:Lecture_1_Intorduction_and_DistanceFunction_ML_2020.pdf ‎|Slides]]
 
  
== Lecture 2 Cluster Analysis 1 ==
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== Week 1 Introduction, Distance function ==
[[Media:Lecture_2_Cluster_Analysis_1_ML_2020.pdf ‎|Slides]]
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[[Media:lecture_01_intorduction_and_distance_function_ml_2024_web_version.pdf ‎|Slides]]
  
== Lecture 3 Cluster Analysis 2 ==
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== Week 2 Cluster analysis I ==
[[Media:Lecture_3_Cluster_Analysis_2_ML_2020.pdf ‎|Slides]]
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[[Media:lecture_02_cluster_analysis_1_ml_2024.pdf ‎|Slides]]
  
== Lecture 4 Supervised learning 1 ==
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== Week 3 Cluster analysis II (Probabilistic approach; Outlier and Anomaly Analysis) ==
[[Media:Lecture_4_Classification_1_ML_2020.pdf ‎|Slides]]
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[[Media:lecture_03_1_cluster_analysis_2_probabilistic_approach_ml_2024.pdf ‎|Slides]]
  
== Lecture 5  Supervised learning 2 ==
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[[Media:lecture_03_2_anomaly_and_otlier_analysis_ml_2024.pdf ‎|Slides]]
[[Media:Lecture_5_Classification_2_ML_2020.pdf ‎|Slides]]
 
  
== Lecture 6  Supervised learning 3 ==
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== Week 4  Supervised learning I: Classification ==
[[Media:Lecture_6_Gradient_descent_andmore_ML_2020.pdf ‎|Slides]]
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[[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]]
 +
 
 +
== Week 6  Supervised learning III: Gradient descent ==
 +
[[Media:lecture_06_Gradient_descent_andmore_ml_2024.pdf ‎|Slides]]
 +
 
 +
[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2024.pdf ‎|Slides]]
 +
 
 +
== Week 7  Supervised learning V: Model quality boosting ==
 +
[[Media:lecture_07_Model_Quality_Boosting_ML_2024.pdf ‎|Slides]]
 +
 
 +
== 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 ]]
 +
[[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 ==
 +
 
 +
 
 +
== 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
  
== Lecture 7  Supervised learning 4 ==
 
[[Lecture_7_Support_Vector_Machines_Kernel_Trick_ML_2020.pdf ‎|Slides]]
 
  
  
==Evaluation==
 
  
 
*91 < score      -- grade 5 (excellent)
 
*91 < score      -- grade 5 (excellent)

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