Копия поста Онлайн-курс "Statistical Learning" из блога Igor Subbotin примеры на "R"

Прочитав это вступлению от Igor Subbotin вы поймете, почему я скопировал весь его пост со всеми ссылками и видео, ...я не тупой спаммер, я недоверчивый жадина:
В январе 2014 Стэнфордский университет провел онлайн-курс, основанный на новой книге An Introduction to Statistical Learning with Applications in R (ISLR).
Курс провели два профессора Trevor Hastie и Rob Tibshirani (автор легендарной книги Elements of Statistical Learning). Это один из лучших курсов по машинному обучению, тоже самое можно сказать и о книге.

Книгу ISLR можно скачать бесплатно в формате PDF.
Также на GitHub можно найти решения заданий из данного курса.

Вступительное видео от авторов курса

Видематериалы и презентации с онлайн-курса "Statistical Learning"

Chapter 1: Introduction (slides, playlist)

• Opening Remarks and Examples (18:18)
• Supervised and Unsupervised Learning (12:12)

Chapter 2: Statistical Learning (slides, playlist)

• Statistical Learning and Regression (11:41)
• Curse of Dimensionality and Parametric Models (11:40)
• Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
• Classification Problems and K-Nearest Neighbors (15:37)
• Lab: Introduction to R (14:12)

Chapter 3: Linear Regression (slides, playlist)

• Simple Linear Regression and Confidence Intervals (13:01)
• Hypothesis Testing (8:24)
• Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
• Model Selection and Qualitative Predictors (14:51)
• Interactions and Nonlinearity (14:16)
• Lab: Linear Regression (22:10)

Chapter 4: Classification (slides, playlist)

• Introduction to Classification (10:25)
• Logistic Regression and Maximum Likelihood (9:07)
• Multivariate Logistic Regression and Confounding (9:53)
• Case-Control Sampling and Multiclass Logistic Regression (7:28)
• Linear Discriminant Analysis and Bayes Theorem (7:12)
• Univariate Linear Discriminant Analysis (7:37)
• Multivariate Linear Discriminant Analysis and ROC Curves (17:42)
• Quadratic Discriminant Analysis and Naive Bayes (10:07)
• Lab: Logistic Regression (10:14)
• Lab: Linear Discriminant Analysis (8:22)
• Lab: K-Nearest Neighbors (5:01)

Chapter 5: Resampling Methods (slides, playlist)

• Estimating Prediction Error and Validation Set Approach (14:01)
• K-fold Cross-Validation (13:33)
• Cross-Validation: The Right and Wrong Ways (10:07)
• The Bootstrap (11:29)
• More on the Bootstrap (14:35)
• Lab: Cross-Validation (11:21)
• Lab: The Bootstrap (7:40)

Chapter 6: Linear Model Selection and Regularization (slides, playlist)

• Linear Model Selection and Best Subset Selection (13:44)
• Forward Stepwise Selection (12:26)
• Backward Stepwise Selection (5:26)
• Estimating Test Error Using Mallow's Cp, AIC, BIC, Adjusted R-squared (14:06)
• Estimating Test Error Using Cross-Validation (8:43)
• Shrinkage Methods and Ridge Regression (12:37)
• The Lasso (15:21)
• Tuning Parameter Selection for Ridge Regression and Lasso (5:27)
• Dimension Reduction (4:45)
• Principal Components Regression and Partial Least Squares (15:48)
• Lab: Best Subset Selection (10:36)
• Lab: Forward Stepwise Selection and Model Selection Using Validation Set (10:32)
• Lab: Model Selection Using Cross-Validation (5:32)
• Lab: Ridge Regression and Lasso (16:34)

Chapter 7: Moving Beyond Linearity (slides, playlist)

• Polynomial Regression and Step Functions (14:59)
• Piecewise Polynomials and Splines (13:13)
• Smoothing Splines (10:10)
• Local Regression and Generalized Additive Models (10:45)
• Lab: Polynomials (21:11)
• Lab: Splines and Generalized Additive Models (12:15)

Chapter 8: Tree-Based Methods (slides, playlist)

• Decision Trees (14:37)
• Pruning a Decision Tree (11:45)
• Classification Trees and Comparison with Linear Models (11:00)
• Bootstrap Aggregation (Bagging) and Random Forests (13:45)
• Boosting and Variable Importance (12:03)
• Lab: Decision Trees (10:13)
• Lab: Random Forests and Boosting (15:35)

Chapter 9: Support Vector Machines (slides, playlist)

• Maximal Margin Classifier (11:35)
• Support Vector Classifier (8:04)
• Kernels and Support Vector Machines (15:04)
• Example and Comparison with Logistic Regression (14:47)
• Lab: Support Vector Machine for Classification (10:13)
• Lab: Nonlinear Support Vector Machine (7:54)

Chapter 10: Unsupervised Learning (slides, playlist)

• Unsupervised Learning and Principal Components Analysis (12:37)
• Exploring Principal Components Analysis and Proportion of Variance Explained (17:39)
• K-means Clustering (17:17)
• Hierarchical Clustering (14:45)
• Breast Cancer Example of Hierarchical Clustering (9:24)
• Lab: Principal Components Analysis (6:28)
• Lab: K-means Clustering (6:31)
• Lab: Hierarchical Clustering (6:33)

Interviews (playlist)

• Interview with John Chambers (10:20)
• Interview with Bradley Efron (12:08)
• Interview with Jerome Friedman (10:29)
• Interviews with statistics graduate students (7:44)

Источник: In-depth introduction to machine learning in 15 hours of expert videos

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