SOURCE: Stanford University
CODE: R programming
Link: https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about
- Ch1 & Ch2 Introduction of Statistical Learning
- KEYWORDS: Opening Remarks | Examples and Framework | Intro Regression Models | Dimensionality and Structured Models | Model Selection and Bias-Variance Tradeoff | Classification
- CODE: Introduction.R
- Ch3 Linear Regression
- KEYWORDS: Simple Linear Regression | Hypothesis Testing and Confidence Intervals | Multiple Linear Regression | Some important questions | Extensions of the linear model
- CODE: LinearRegression.R
- Ch4 Classification
- KEYWORDS: Intro Classification Problems | Logistic Regression | Multivariate Logistic Regression | Case-Control Sampling and Multiclass | Discriminant Analysis | Gaussian Discriminant Analysis | Quadratic Discriminant Analysis and Naive Bayes
- CODE: LogisticRegression.R - LDA.R - KNN.R
- Ch5 Resampling Mehtods
- KEYWORDS: Cross-validation | K-fold Cross-Validation | Bootstrap
- CODE: CrossValidation.R - Bootstrap.R - 5ReviewQuestions.R
- Ch6 Model Selection
- KEYWORDS: Subset Selection | Forward Stepwise Selection | Backward Stepwise Selection | Cp | AIC | BIC | Adjusted R^2 | Validation | Cross-Validation | Ridge regression | Lasso | Principal Components Regression | Partial Least Squares
- CODE: ModelSelection.rmd
- Ch7 Non Linearity
- KEYWORDS: Polynimial Regression | Step Functions | Piecewise Polynimials | Linear Splines | Cubic Splines | Smoothing Splines | Local Regression | Generalized Additive Models
- CODE: NonlinearFunctions.Rmd
- Ch8 Tree-based Methods
- KEYWORDS: Decision Trees | Pruning a tree | Classification Trees | Bagging | Random Forests | Boosting
- CODE: FittingTrees.Rmd
- Ch9 Support Vector Machines
- KEYWORDS: Separating Hyperplanes | Maximal Margin Classifier | Non-separable Data | Noisy Data | Support Vector Classifier | Regularization parameter | Cubic Polynomials | Nonlinearities and Kernels
- CODE: svm.Rmd - 9R_ReviewQuestions.R
- Ch10 Unsupervised Learning
- KEYWORDS: Unsupervised Learning | Principal Components Analysis | Clustering | K-means clustering | Hierarchical clustering
- CODE: PCA_Clustering.Rmd - 10R_ReviewQuestions.R