- 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 |