STA 7233: Advanced Regression
Lecture. 12:50 PM - 2:45 PM (Tues), 12:50 PM - 1:40 PM (Thurs)
Instructor. Aaron Molstad (amolstad@ufl.edu), 202 Griffin-Floyd
Virtual office hours. TBA [Zoom link]
In-person/virtual office hours. 1:50 PM - 3:50 PM (Thurs, 202 Griffin-Floyd)
Syllabus. [pdf] Lecture link. [link]
Note that you must be logged into your UFL eLearning account to access course materials.
| Lecture | Topics | |||
| 0 (8/25) | Syllabus, ordinary least squares, ridge regression [slides][lecture] | |||
| 1 (8/30) | Ridge regression, cross-validation, lasso [slides][lecture] | |||
| 2 (9/1) | Lasso computation [slides][lecture] | |||
| 3 (9/6) | Lasso variants and theoretical properties [slides][lecture] | |||
| 4 (9/8) | Theory for the lasso [slides][lecture] | |||
| 5 (9/13) | Support recovery, debiasing, knockoffs [slides][lecture][error] |
Homework 1 (Due Sunday, September 18th at 5:00pm) [pdf][submit]
Linear regression notes [pdf] (updated on Sunday, October 16th)
| 6 (9/15) | Knockoffs, basis functions, splines [slides][lecture][error] | |||
| 7 (9/20) | Order-M splines, smoothing spline estimator [slides][lecture] | |||
| 8 (9/22) | RKHS and the representer theorem [slides][lecture] | |||
| 9 (9/27) | RKHS, kernel ridge regression, and kernel smoothers [slides][lecture] |
Homework 2 (Due Tuesday, October 4th at 5:00pm) [pdf][submit]
Semiparametrics notes [pdf]
| 10 (10/4) | Discriminant analysis [slides][lecture] | |||
| 11 (10/6) | Separating hyperplanes [slides] | |||
| 12 (10/13) | Support vector machines [slides][lecture] |
Homework 3 (Due Sunday, October 16th at 5:00pm) [pdf][submit]
Classification notes [pdf]
| 13 (10/18) | Support vector machines continued [lecture][lecture video] | |||
| 14 (10/20) | Generalized additive models [slides][lecture] | |||
| 15 (10/25) | Bagging and random forests [slides][lecture] | |||
| 16 (10/27) | Boosting [slides][lecture] |
Homework 4 (Due Tuesday, November 1st at 5:00pm) [pdf][submit]
Project proposal [pdf][submit]
| 17 (11/1) | Gradient boosting trees, ensemble method, PPR [slides] | |||
| 18 (11/3) | Neural networks [slides][lecture] | |||
| 19 (11/8) | Deep learning, double descent [slides][lecture] |
| 20 (11/17) | Graphical models [slides] |
Tree-based methods notes [pdf]
Ensemble methods notes [pdf]
Neural networks note [pdf]
Homework 5 (Due Friday, November 18th at 5:00pm) [pdf][submission]
| 21 (11/22) | Graphical models and multivariate regression [slides] | |||
| 22 (11/29) | Multivariate regression [slides] |
Homework 6 (Due Monday, December 7th at 5:00pm) [pdf][submission]
Project rubric [pdf]
Project report [submission]