Statistical Learning Theory
- Overview of the Course
- Linear Regression: Single and Multivariable Linear Regression
- Classification: Logistic Regression, Discriminant Analysis
- Resampling Methods: Cross-validation, Bootstrap
- Model Selection: Subset Selection, Shrinkage Method, Dimension Reduction
- Nonlinear Schemes: Splines, Tree-based methods, Support Vector Machines
- Deep Learning: Neural Net models, Deep Tree-based methods, Restricted Boltzmann Machines, Autoencoders
- Unsupervised Learning: PCA, K-means Clustering
- Semi-supervised Learning: Co-training, Graph-Based Learning