Introduction to Machine Learning and Data Mining — различия между версиями

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Lecturers: Dmitry Ignatov
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TAs: Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).
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=== Lecture on 23.01.2019===
 
=== Lecture on 23.01.2019===
  
Intro slides
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Intro slides.
  
 
Practice: demonstration with Orange.
 
Practice: demonstration with Orange.
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=== Lecture on 06.02.2019===
 
=== Lecture on 06.02.2019===
  
Introduction to classification techniques
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Slides: Introduction to classification techniques (1-rule, kNN, Naive Bayes, Logistic Regression).
  
 
Practice: demonstration with Orange and scikit-learn.
 
Practice: demonstration with Orange and scikit-learn.
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=== Lecture on 22.02.2019 ===
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Practice with scikit-learn (kNN, Naive Bayes, Logistic Regression, basic quality metrics, cross-validation, error plots)
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Slides: Decision trees. Entropy and information gain. ID3 algorithm. Gini impurity. Tree pruning.

Версия 10:23, 22 февраля 2019

Lecturers: Dmitry Ignatov

TAs: Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).

Lecture on 23.01.2019

Intro slides.

Practice: demonstration with Orange.

Lecture on 06.02.2019

Slides: Introduction to classification techniques (1-rule, kNN, Naive Bayes, Logistic Regression).

Practice: demonstration with Orange and scikit-learn.

Lecture on 22.02.2019

Practice with scikit-learn (kNN, Naive Bayes, Logistic Regression, basic quality metrics, cross-validation, error plots)

Slides: Decision trees. Entropy and information gain. ID3 algorithm. Gini impurity. Tree pruning.