| Date | Content | Reading | Slides |
|---|---|---|---|
| Th 9/25 | Welcome/Overview; Maximum likelihood estimation |
Probability review: Murphy 2.1-2.4, 2.6, 2.8, 3.1-3.2 Statistics review, maximum likelihood: Murphy 4.2 |
(slides) |
| Supervised learning: linear models | |||
| Tu 9/30 | Linear regression |
Linear algebra review: Murphy 7.1-7.3 Matrix calculus review: Murphy 7.8 Maximum likelihood regression: Murphy 4.2 Linear regression: Murphy 11-11.2 |
(slides, pre-annotated slides, live-annotated slides) |
| Th 10/2 | Linear regression with basis functions; Cross-validation |
Maximum likelihood regression: Murphy 4.2 Linear regression: Murphy 11-11.2 |
(slides, pre-annotated slides, live-annotated slides), (polynomial demo, linear demo, diabetes.txt) |
| Tu 10/7 | Bias-variance trade-off | Bias-variance trade-off: Murphy 4.7.6 | (slides, pre-annotated slides, live-annotated slides ), (bias-variance tradeoff demo ) |
| Th 10/9 | Regularization | Ridge regression: Murphy 11.3-11.4 | (slides, pre-annotated slides, live-annotated slides), |
| Tu 10/14 | Sparsity and LASSO; Gradient descent |
Lasso regression: Murphy 11.4 Gradient descent: Murphy 8-8.2.1 |
(slides,
annotated slides
) gradient descent demo .ipynb, .html |
| Th 10/16 | Convexity; Gradient descent analysis; Stochastic gradient descent | Stochastic gradient descent: Murphy 8.4-8.4.4 | (slides, annotated slides ) |
| Tu 10/21 | Classification; Logistic regression | Logistic regression: Murphy 10-10.2.4, 10.3-10.3.3 | (slides, annotated slides ) |
| Th 10/23 | Classification 2; prediction pitfalls | Classification pitfalls: Murphy 10.4 | (slides, annotated slides) |
| Tu 10/28 | Midterm | See exam page | |
| Supervised learning: non-linear models | |||
| Th 10/30 | Bootstrap; Kernel methods |
Bootstrap: Efron and Hastie 10.2, 11-11.2 Kernels: Bishop 6-6.2, Murphy 17, 17.1, 17.3.4, 17.3.9 |
(slides, pre-annotated slides, live-annotated slides ) |
| Tu 11/4 | Neural networks | Neural networks: Murphy 13-13.4.3 | (slides, pre-annotated slides, live-annotated slides ) Tensorflow Playground |
| Th 11/6 | Non-parametric methods; Nearest neighbors | Nearest neighbors: Murphy 16.1 | (slides, annotated slides) |
| Tu 11/11 | Veterans Day, No class | ||
| Th 11/13 | More non-parametric methods; Tree-based |
Trees, Random Forests: Murphy 18 Gradient Boosting Trees: Murphy 18 |
(annotated slides) |
| Unsupervised learning | |||
| Tu 11/18 | Principal component analysis (PCA) |
PCA, Singular value decomposition: Murphy 20.1 Kernel PCA: Murphy 20.4.6 |
(slides, live-annotated slides) |
| Th 11/20 | Singular value decomposition (SVD); more matrix decompositions; autoencoders | Autoencoders: Murphy 20.3, 22.1 | (slides, live-annotated slides) |
| Tu 11/25 | K-means; Gaussian mixture models (GMMs) | K-means, GMM: Murphy 21.3-21.5 | (slides, annotated slides) |
| Modern machine learning | |||
| Th 11/27 | Thanksgiving, No Class | ||
| Tu 12/2 | Guest lecture by Leo: Multi-armed bandits | (slides) | |
| Th 12/4 | Foundation models | (slides, annotated slides) | |
| M 12/8, 10:30am | Final Exam | See exam page | |