Introduction to Machine Learning and Data Mining: различия между версиями
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Machine (обсуждение | вклад) Нет описания правки |
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Lecturers: Dmitry Ignatov | |||
TAs: Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4). | |||
=== Lecture on 23.01.2019=== | === Lecture on 23.01.2019=== | ||
Intro slides | Intro slides. | ||
Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
| Строка 7: | Строка 11: | ||
=== Lecture on 06.02.2019=== | === Lecture on 06.02.2019=== | ||
Introduction to classification techniques | 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. | ||
=== 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. | |||
Версия от 07:23, 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.