Math637 – Spring 2020
Time & Place: MWF 12:20PM – 1:10PM, Room: EWG 204 (Ewing Hall)
Syllabus: pdf version
Textbook: T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
Available for free at http://statweb.stanford.edu/~tibs/ElemStatLearn/.
Lectures:
| Date | Topic | Slides |
| February 10 | Overview | |
| February 12 | Linear Regression: old and new | |
| February 17 | Linear Regression: old and new (part 2) | |
| February 24 | Consistency of Linear Regression | |
| February 26 | Subset selection and Coefficients Penalization | |
| March 2 | Model selection | |
| March 4 | Computing the lasso solution | |
| March 9 | Categorical data | |
| March 11 | Introduction to statistical decision theory | |
| March 30, April 1 | Logistic regression & Linear Discriminant Analysis | |
| April 6 | Support vector machines | |
| April 10 | Support vector machines and kernels | |
| April 13 | Principal component analysis | |
| April 15 | Decision trees | |
| April 17 | Lab | |
| April 20 | Random forest | |
| April 22 | Neural networks I | |
| April 24 | Neural networks II | |
| April 27 | Neural networks Lab | |
| April 29, May 1 | The singular value decomposition | |
| May 4, 6 | Clustering I | |
| May 8 | Clustering II | |
| May 11 | Clustering Lab | |
| May 13 | The EM algorithm | |
| May 15 | Independent component analysis | |
| May 21 | Project presentations | schedule |