Statistical Learning: 1.1 Opening Remarks Statistical Learning: 8 Years Later (Second Edition of the Course) Statistical Learning: 1.2 Examples and Framework Statistical Learning: 2.1 Introduction to Regression Models Statistical Learning: 2.2 Dimensionality and Structured Models Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff Statistical Learning: 2.4 Classification Statistical Learning: 2.R Introduction to R Statistical Learning: 3.1 Simple linear regression Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals Statistical Learning: 3.3 Multiple Linear Regression Statistical Learning: 3.4 Some important questions Statistical Learning: 3.5 Extensions of the Linear Model Statistical Learning: 3.R Regression in R Statistical Learning: 4.1 Introduction to Classification Problems Statistical Learning: 4.2 Logistic Regression Statistical Learning: 4.3 Multivariate Logistic Regression Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass Statistical Learning: 4.5 Discriminant Analysis Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable) Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables) Statistical Learning: 4.8 Generalized Linear Models Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes Statistical Learning: 4.R.1 Logistic Regression Statistical Learning: 4.R.2 Linear Discriminant Analysis Statistical Learning: 4.R.3 Nearest Neighbor Classification Statistical Learning: 5.1 Cross Validation Statistical Learning: 5.2 K-fold Cross Validation Statistical Learning: 5.3 Cross Validation the wrong and right way Statistical Learning: 5.4 The Bootstrap Statistical Learning: 5.5 More on the Bootstrap Statistical Learning: 5.R.1 Cross Validation Statistical Learning: 5.R.2 Bootstrap Statistical Learning: 6.1 Introduction and Best Subset Selection Statistical Learning: 6.2 Stepwise Selection Statistical Learning: 6.3 Backward stepwise selection Statistical Learning: 6.4 Estimating test error Statistical Learning: 6.5 Validation and cross validation Statistical Learning: 6.6 Shrinkage methods and ridge regression Statistical Learning: 6.7 The Lasso Statistical Learning: 6.8 Tuning parameter selection Statistical Learning: 6.9 Dimension Reduction Methods Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares Statistical Learning: 6.R.1 Markdown in RStudio and Best Subset Regression Statistical Learning: 6.R.2 Forward Stepwise Regression Statistical Learning: 6.R.3 Model Selection and Cross-Validation Statistical Learning: 6.R.4 Ridge Regression and Lasso Statistical Learning: 7.1 Polynomials and Step Functions Statistical Learning: 7.2 Piecewise Polynomials and Splines Statistical Learning: 7.3 Smoothing Splines Statistical Learning: 7.4 Generalized Additive Models and Local Regression Statistical Learning: 7.R.1 Polynomials in GLMs Statistical Learning: 7.R.2 Splines and GAMs Statistical Learning: 8.1 Tree based methods Statistical Learning: 8.2 More details on Trees Statistical Learning: 8.3 Classification Trees Statistical Learning: 8.4 Bagging Statistical Learning: 8.5 Boosting Statistical Learning: 8.6 Bayesian Additive Regression Trees Statistical Learning: 8.R.1 Fitting Trees Statistical Learning: 8.R.2 Random Forests and Boosting Statistical Learning: 9.1 Optimal Separating Hyperplane Statistical Learning: 9.2.Support Vector Classifier Statistical Learning: 9.3 Feature Expansion and the SVM Statistical Learning: 9.4 Example and Comparison with Logistic Regression Statistical Learning: 9.R.1 Support Vector Classifier Statistical Learning: 9.R.2 Nonlinear Support Vector Machine Statistical Learning: 10.1 Introduction to Neural Networks Statistical Learning: 10.2 Convolutional Neural Networks Statistical Learning: 10.3 Document Classification Statistical Learning: 10.4 Recurrent Neural Networks Statistical Learning: 10.5 Time Series Forecasting Statistical Learning: 10.6 Fitting Neural Networks Statistical Learning: 10.7 Interpolation and Double Descent Statistical Learning: 10.R.1 Neural Networks in R and the MNIST data Statistical Learning: 10.R.2 Convolutional Neural Networks in R Statistical Learning: 10.R.3 Document Classification Statistical Learning: 10.R.4 Recurrent Neural Networks Statistical Learning: 11.1 Introduction to Survival Data and Censoring Statistical Learning: 11.2 Proportional Hazards Model Statistical Learning: 11.3 Estimation of Cox Model with Examples Statistical Learning: 11.4 Model Evaluation and Further Topics Statistical Learning: 11.R.1 Survival Curves Brain Cancer Data Statistical Learning: 11.R.2 Cox Models I Publication Data Statistical Learning: 11.R.3 Cox Models II Call Center Data Statistical Learning: 12.1 Principal Components Statistical Learning: 12.2 Higher order principal components Statistical Learning: 12.3 k means Clustering Statistical Learning: 12.4 Hierarchical Clustering Statistical Learning: 12.5 Matrix Completion Statistical Learning: 12.6 Breast Cancer Example Statistical Learning: 12.R.1 Principal Components Statistical Learning: 12.R.2 K means Clustering Statistical Learning: 12.R.3 Hierarchical Clustering Statistical Learning: 13.1 Introduction to Hypothesis Testing Statistical Learning: 13.1 Introduction to Hypothesis Testing II Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate Statistical Learning: 13.3 Bonferroni Method for Controlling FWER Statistical Learning: 13.4 Holm's Method for Controlling FWER Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method Statistical Learning: 13.6 Resampling Approaches Statistical Learning: 13.6 Resampling Approaches II Statistical Learning: 13.R.1 Bonferroni and Holm Statistical Learning: 13.R.1 Bonferroni and Holm II