Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

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If necessary update versions of the lectures will be distributed among the students via course page in TalTech Moodle environment!!!
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If necessary updated versions of the lectures will be distributed among the students via course page in TalTech Moodle environment!!!
 
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Redaktsioon: 23. jaanuar 2023, kell 16:22

Machine learning ITI8565

Spring term 2023

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0


Lectures on Tuesdays 15:30-17:00 ICT-A1

Practices on Thursdays 16:30-17:00 ICT-401

Precise descriptions of the home assignments and supplementary files will be distributed via TalTech Moodle environment ONLY!!!
If necessary updated versions of the lectures will be distributed among the students via course page in TalTech Moodle environment!!!

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

05.03.2023 23:59 Deadline to submit home assignment I!!!

Home Assignment I

Week 6 Supervised learning III: Gradient descent

Slides

Week 7 Supervised learning IV: Support Vector Machine

Slides

Week 8 Supervised learning V: Model quality boosting

Slides

Week 9 Markov Models

Slides

30.03.2023 Test I!!!
02.04.2023 23:59 Deadline to submit home assignment II!!!

Home Assignment II


Week 10 Neural Networks I

Slides Slides


Week 11 Neural Networks II

Slides

Week 12 Deep Learning I: Sequential Models

TBP

Week 13 Deep Learning II: Convolutional neural networks

TBU Slides

Week 14 Deep Learning II: Transformers

TBU Slides


14.05.2023 23:59 Deadline to submit home assignment III!!!

Home Assignment III

Week 16

16.05.2023Test II!!!


  • 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