Slides

Principal Component Analysis

1. Basics: Orthogonal Projections
   

2. One-dimensional PCA
   

3. PCA - Projection onto M-dimensional Subspace
   

4. PCA under An Optimization Lens
   

5. PCA for High-dimensional Data
   

6. Low-Rank Matrix Approximations, SVD and Connections to PCA
   

7. Ridge Regression and PCA
   

8. Sparse PCA
   

9. Matrix Factorization and Completion
   

Convex Optimization

Study notes for Ryan Tibshirani's Convex Optimization course

1. Convex Sets
   

2. Convex Functions
   

3. Convex Optimization Problems
   

4. Gradient Descent
   

5. Subgradient Methods
   

6. Duality
   

7. KKT Conditions
   

8. Dual Problems
   

9. Proximal Methods
   

10. ADMM Algorithms
    

11. Lasso Screening
    

Support Vector Machines

Study notes for Chapter 12 of The Elements of Statistical Learning

1. Study notes
   

Cross Validation

Study notes for Chapter 7.10 of The Elements of Statistical Learning

1. Study notes