Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
 
(ei näidata sama kasutaja 10 vahepealset redaktsiooni)
15. rida: 15. rida:
 
Consultations is by appointment only!  Please do not hesitate to ask for consultation!  
 
Consultations is by appointment only!  Please do not hesitate to ask for consultation!  
  
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Information for perspective students:
 
You are welcome to join the course by means of ÕIS!
 
On January the 29thth around afternoon ÕIS will generate welcome e-mail with the instructions to join Moodle page of the course.
 
 
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<pre style="color: red">
 
Slides below are mostly from the year 2023. You are welcome to use this material as the reference but be aware that this year the course content will be revised and a few news topics will be added. 
 
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=Lectures =
 
=Lectures =
44. rida: 34. rida:
 
== Week 5  Supervised learning II: Regression  ==
 
== Week 5  Supervised learning II: Regression  ==
 
[[Media:lecture_05_supervised_learning_2_ml_2024.pdf ‎|Slides]]
 
[[Media:lecture_05_supervised_learning_2_ml_2024.pdf ‎|Slides]]
 
<!--[[Media:HA_01_ML_2023_web_version.pdf ‎|Home Assignment I]] -->
 
  
 
== Week 6  Supervised learning III: Gradient descent ==
 
== Week 6  Supervised learning III: Gradient descent ==
[[Media:Lecture_06_Gradient_descent_andmore_ML_2023.pdf ‎|Slides]]
+
[[Media:lecture_06_Gradient_descent_andmore_ml_2024.pdf ‎|Slides]]
 
 
== Week 7  Supervised learning IV: Support Vector Machine ==
 
[[Media:Lecture_07_Support_Vector_Machines_Kernel_Trick_ML_2023.pdf ‎|Slides]]
 
 
 
== Week 8  Supervised learning V: Model quality boosting ==
 
[[Media:Lecture_08_Model_Quality_Boosting_ML_2023.pdf ‎|Slides]]
 
 
 
== Week 9  Markov Models ==
 
[[Media:Lecture_09_Hidden_Markov_Models_ML2023.pdf ‎|Slides]]
 
 
 
<!--<pre style="color: red"> -->
 
<!--30.03.2023 Test I!!! -->
 
<!--</pre> -->
 
  
<!--<pre style="color: red"> -->
+
[[Media:lecture_06_Support_Vector_Machines_Kernel_Trick_ML_2024.pdf ‎|Slides]]
<!--02.04.2023 23:59 Deadline to submit home assignment II!!! -->
 
<!--</pre> -->
 
<!--[[Media:Home_Assignment_02_ML_2023_web_version.pdf ‎|Home Assignment II]] -->
 
  
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== Week 7  Supervised learning V: Model quality boosting ==
 +
[[Media:lecture_07_Model_Quality_Boosting_ML_2024.pdf ‎|Slides]]
  
== Week 10 Neural Networks I ==
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== Week 8 Closed book test 1 ==
[[Media: Lecture_10_Neural_Networks_ML_2023.pdf ‎|Slides]]
 
[[Media: Lecture_10_part_2_Neural_Networks_ML_2023.pdf ‎|Slides]]
 
  
 +
== 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 11 Neural Networks II ==
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== Week 10 Sequential processes modelling: from Markov Models to LSTM ==
[[Media: Lecture_11_Neural_Networks_2_ML_2023.pdf ‎|Slides]]
 
  
== Week 12  Deep Learning I: Sequential Models==
 
TBP
 
  
== Week 13 Deep Learning II: Convolutional neural networks==
+
== Week 11  Deep Learning I: Transformers==
TBU [[Media:Lecture_14_Deep_Learning_CNN_ML_2022.pdf ‎|Slides]]
+
TBA
  
== Week 14 Deep Learning II: Transformers==
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== Week 12 Deep Learning II: Convolutional neural networks==
TBU [[Media:Lecture_15_Transformers_ML_2022.pdf ‎|Slides]]
+
TBA
  
 +
== Week 13 Deep Learning III: Generative AI ==
 +
TBA
  
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== Week 14 Explainable AI==
<!--14.05.2023 23:59 Deadline to submit home assignment III!!! -->
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TBA
<!--</pre> -->
 
<!--[[Media: HA_3_ML_2023_web_version.pdf ‎|Home Assignment III]] -->
 
  
  

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