- Elements of Statistical Learning (ESL)
- An Introduction to Statistical Learning with Applications in R (ISLR)
| Date | Topics | Handouts | Book | Other Readings |
|---|---|---|---|---|
| 8/27 | Introduction | lecture | ISLR 1 | |
| 9/1 | Linear models | lecture | ISLR 2 | Linear discriminant and support vector classifiers |
| 9/3 | Support Vector Machines | lecture | ISLR 2 and 9 | Introduction to Large Margin Classifier |
| 9/4 | Discussion 1 | handout, solutions | ||
| 9/8 | Risk minimization, Learn opt. margin Perceptron via gradient descent | lecture | Large scale ML with SGD | |
| 9/10 | Shrinkage | lecture | ISLR 6.2 | |
| 9/11 | Discussion 2 | handout, solutions, slides | ||
| 9/15 | Bayesian decision theory and Logistic regression | lecture | ISLR 4 | |
| 9/17 | Ridge regression | lecture | ISLR 3 | Kernel Ridge Regression |
| 9/18 | Discussion 3 | handout, solutions, slides | ||
| 9/22 | Kernel methods | lecture | Kernel methods (chatper 2 and 3) | |
| 9/24 | Performance evaluation | lecture | ISLR 5 | |
| 9/25 | Discussion 4 | handout, solutions, slides | ||
| 9/29 | Model selection (1) | lecture | ||
| 10/1 | Model selection (2) | lecture | ISLR Ch 6 | |
| 10/2 | Discussion 5 | handout, solutions, slides | ||
| 10/6 | Gaussian classifier | lecture | ||
| 10/8 | LDA | lecture | ISLR Ch 4, Ch 10.2 for PCA | |
| 10/9 | Discussion 6 | handout, solutions, slides | ||
| 10/13 | Gaussian mixtures | lecture | ||
| 10/15 | Gaussian processes | lecture | GP tutorial | |
| 10/16 | Discussion 7 | handout, solutions, slides | ||
| 10/20 | Non-parametric methods | lecture | ||
| 10/22 | Curse of dimensionality | lecture | ||
| 10/23 | Discussion 8 | handout, solutions | ||
| 10/27 | Midterm | solutions | ||
| 10/29 | Decision Trees | lecture | ISLR 8.1 | |
| 11/30 | Discussion 9 | handout, solutions | ||
| 11/3 | Decision Trees #2 | lecture | ISLR 8.1 | |
| 11/5 | Ensemble Methods | [lecture](./lecture/Ensemble Methods.pdf) | ISLR 8.2 | |
| 11/6 | Discussion 10 | handout, solutions | ||
| 11/10 | Neural Networks | lecture | A chapter from Daume's almost-finished ML textbook, A new online neural network textbook by Nielsen, Neural Networks Demystified | |
| 11/12 | Training Neural Nets | lecture | ||
| 11/17 | Convolutional Neural Nets | lecture | Convolutional Networks, Convolutional Neural Network | |
| 11/19 | Clustering | lecture | ISL 10.1, 10.3; ESL 14.3 | |
| 11/20 | Discussion 11 | handout, solutions | ||
| 11/24 | PCA, collaborative filtering | lecture | ESL 6.6.1, 14.2-14.2.3; ISL 10.2 | |
| 12/1 | Finish PCA, density estimation, associative rules | [lecture](./lecture/Mode Finding.pdf) | ||
| 12/4 | Discussion 12 | handout, solutions |
| Due | Topic |
|---|---|
| 9/11 | SVM |
| 9/24 | Probability, Linear Algebra, Matrix Calculus |
| 10/6 | Linear and Logistic Regression |
| 10/18 | Gaussian Classifiers |
| 11/9 | Decision Trees and Random Forests |
| 11/24 | Neural Networks |
| 12/4 | Unsupervised Learning |