Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
48. rida: 48. rida:
 
[[Media:Lecture_09_Model_Quality_Boosting_ML_2022.pdf ‎|Slides]]
 
[[Media:Lecture_09_Model_Quality_Boosting_ML_2022.pdf ‎|Slides]]
  
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== Week 10  Supervised learning IV: Model quality boosting ==
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[[Media:Markov model (1) (1).pdf ‎|Slides]]
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== Week 11  Supervised learning IV: Model quality boosting ==
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[[Media: Lecture_11_Neural_Networks_ML_2022.pdf ‎|Slides]]
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== Week 12  Supervised learning IV: Model quality boosting ==
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[[Media:Lecture_12_Neural_Networks_ML_2022.pdf ‎|Slides]]
  
 
*91 < score      -- grade 5 (excellent)
 
*91 < score      -- grade 5 (excellent)

Redaktsioon: 12. aprill 2022, kell 10:38

Machine learning ITI8565

Spring term 2022

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0

For the month of March the course will continue purely in online mode!!!

Lectures on Tuesdays 13:45-15:15 Online only in MS Teams environment

Practices on Thursdays 13:40-15:10 Online only in MS Teams environment

Please use code HAL900 to join TalTech Moodle page of the course.


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: Feature selection kNN and regression

Slides

Week 5 Supervised learning II: Regression and decision trees

Slides

Week 6 Supervised learning III: Gradient descent

Slides

Week 7 Supervised learning IV: Support Vector Machine

Slides

Week 9 Supervised learning IV: Model quality boosting

Slides

Week 10 Supervised learning IV: Model quality boosting

Slides

Week 11 Supervised learning IV: Model quality boosting

Slides

Week 12 Supervised learning IV: Model quality boosting

Slides

  • 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