My workspace for working through the "Introduction to Statistical Learning" course and corresponding book.
Course website: edx.org
All resources are also available at the book's website: statlearning.com
Current status: DONE
- Chapter 1 - Introduction
- Chapter 2 - Statistical Learning
- Prediction Accuracy vs. Model Interpretability
- Supervised vs. unsupervised
- Regression vs. Classification
- Chapter 3 - Linear Regression
- Simple Linear Regression
- Multiple Linear Regression
- Qualitative Predictors
- Extensions
- Chapter 4 - Classification
- (Multiple) Logistic Regression
- Linear Discriminant Analysis
- Quadratic Discriminant Analysis
- Naive Bayes
- Poisson Regression
- Chapter 5 - Resampling Methods
- Cross-validation
- Bootstrap
- Chapter 6 - Linear Model Selection and Regularization
- Best-subset Selection
- Shrinkage Methods
- Dimension Reduction Methods
- Chapter 7 - Moving Beyond Linearity
- Polynomials and Step Functions
- Piecewise Polynomials, Splines, and Smoothing Splines
- Local Regression
- Generalized Additive Models
- Chapter 8 - Tree-Based Methods
- Bagging
- Boosting
- Random Forests
- BART
- Chapter 9 - Support Vector Machines
- Chapter 10 - Deep Learning
- CNNs
- RNNs
- Chapter 11 - Survival Analysis and Censored Data
- Kaplan-Meier curve
- Log-Rank test
- Cox proportional hazards model
- Chapter 12 - Unsupervised Learning
- Hierarchical Clustering
- kMeans
- Chapter 13 - Multiple Testing
- Family-Wise Error Rate
- False Discovery Rate