Schedule is approximate. Links to slides are broken until the slides are posted. Not every lecture will have slides.
| Week | Date | Topic | Materials | |
|---|---|---|---|---|
| 1 | Lec 01 | 1/22 | Intro | HW0 due Friday, 2/1 [moodle] See CS335 Python Guide |
| Lec 02 | 1/24 | Linear regression in one variable | Calculus Review | |
| 2 | Lec 03 | 1/29 | Gradient descent / Python Demo: [ipynb] [html] |
|
| Lec 04 | 1/31 | Linear algebra Slides/demo: [ipynb] [html] |
HW 1 out [pdf] [files] | |
| 3 | Lec 05 | 2/5 | Multivariate linear regression Vectorized code demo: [ipynb] [html] |
|
| Lec 06 | 2/7 | Normal equations and vectorized gradient descent | HW 2 out [pdf] [files] [pprint_hw2.py] | |
| 4 | Lec 07 | 2/12 | Logistic regression | |
| Lec 08 | 2/14 | Nonlinearity, overfitting, regularization | HW 3 out [pdf] [files] | |
| 5 | Lec 09 | 2/19 | Multiclass classification scikit-learn classification demo: [html] [ipynb] [data]) |
|
| Lec 10 | 2/21 | Generalization and cross-validation | ||
| 6 | Lec 11 | 2/26 | KNN and decision trees Demo [html] [ipynb] [data] |
|
| Lec 12 | 2/28 | Kernel Trick Demo [html] [ipynb] |
HW 4 out [pdf] [files] | |
| 7 | Lec 13 | 3/5 | Kernel Trick | project guidelines |
| Lec 14 | 3/7 | Cross-Entropy Loss / Autograd Intro (board work) | ||
| SPRING BREAK — WEEK OF 3/11 | ||||
| 8 | Lec 15 | 3/19 | Neural nets | |
| Lec 16 | 3/21 | Neural nets (backprop) Backprop demo [html] [ipynb] |
||
| 9 | Lec 17 | 3/26 | Neural nets Demo [html] [ipynb] |
|
| Lec 18 | 3/28 | Movie recommendations | Movie recommendations [zip] | |
| 10 | Lec 19 | 4/2 | Movie recommendations | |
| Lec 20 | 4/4 | Movie recommendations | ||
| 11 | 4/9 | NO CLASS — COMMUNITY DAY | ||
| Lec 21 | 4/11 | PCA | ||
| 12 | Lec 22 | 4/16 | Bayesian classification | |
| Lec 23 | 4/18 | Clustering | ||
| 13 | 4/23 | NO CLASS — WORK ON PROJECTS | ||
| Lec 24 | 4/25 | Gaussian mixture models and EM. (Slides: Lec 23) | ||
| 14 | Lec 25 | 4/30 | Fairness in ML |