ОУФ Машинное Обучение в Питоне — различия между версиями

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'''Технические требования''': Laptop, Internet connection, Chrome web browser, Google Drive, Google Colab.
 
'''Технические требования''': Laptop, Internet connection, Chrome web browser, Google Drive, Google Colab.
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== План курса ==
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=== Лекции ===
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1. Math Essentials. Intro to Python in Google Colab
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2. Ch2. Intro to Statistical learning
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3. Ch3. Linear Regression
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4. Ch3. k-Nearest Neighbors
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5. Ch4. Classification: logistic regression
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6. Ch4. Classification: LDA, QDA, KNN
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7. Ch5. Resampling methods. CV, Bootstrap
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8. Ch6. Linear model selection & regularization
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9. Ch7. Non-linear regression
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10. Ch7. Non-linear regression-2
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11. Ch8. Decision Trees
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12. Ch8. Bagging, Random Forest, Boosting
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13. Ch9. Support Vector Machines/Classifiers
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14. Ch10. Clustering methods. PCA, k-Means, HC
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15. Special Topics: tSNE, UMAP, Neural Networks

Версия 18:43, 3 сентября 2020

О курсе

Курс читается в 1-2 модулях.

Instructor: Oleg Melnikov

Ассистенты: see Canvas LMS

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods and much more. The two modules (Sept-Dec, 2020) use Python programming language and popular packages to investigate and visualize datasets and develop machine learning models.

Пререквизиты курса: at least one semester of calculus on a real line, vector calculus, linear algebra, probability and statistics, computer programming in high level language such as Python or R.

Технические требования: Laptop, Internet connection, Chrome web browser, Google Drive, Google Colab.

План курса

Лекции

1. Math Essentials. Intro to Python in Google Colab

2. Ch2. Intro to Statistical learning

3. Ch3. Linear Regression

4. Ch3. k-Nearest Neighbors

5. Ch4. Classification: logistic regression

6. Ch4. Classification: LDA, QDA, KNN

7. Ch5. Resampling methods. CV, Bootstrap

8. Ch6. Linear model selection & regularization

9. Ch7. Non-linear regression

10. Ch7. Non-linear regression-2

11. Ch8. Decision Trees

12. Ch8. Bagging, Random Forest, Boosting

13. Ch9. Support Vector Machines/Classifiers

14. Ch10. Clustering methods. PCA, k-Means, HC

15. Special Topics: tSNE, UMAP, Neural Networks

Источник — «http://wiki.cs.hse.ru/index.php?title=ОУФ_Машинное_Обучение_в_Питоне&oldid=43261»