| Week |
Topics |
Homework |
Reading |
Special Events and Due Dates |
Slides & Lecture Notes |
| 1 (8/28+30,9/1) |
Introduction, Review: linear algebra, probability |
|
DL Ch. 1 & Ch. 2 |
|
Slides (1), (2), (3), (4) |
2 (9/6+8) |
Review: Stochastic processes, distributions, information theory |
|
DL Ch. 3
| |
Slides (1), (2), (3) |
3 (9/11+13+15) |
Optimization, foundational principles of ML |
|
DL Ch. 4 & Ch. 5 |
|
Slides (1), (2), (3), (4) |
4 (9/18+20+22) |
Learning theory |
|
DL Ch. 5 |
|
Slides (1), (2), (3), (4) |
5 (9/25+27+29) |
Supervised learning: Linear regression, polynomial regression |
|
|
HW #0 due 10/6 |
Slides (1), (2), (3) |
6 (10/2+4+6) |
Logistic regression (LR) and classification |
|
|
|
Slides (1), (2), (3) |
7 (10/9+11+13) |
Discriminative modeling with linear classifiers |
|
|
HW #1 due 10/26 |
Slides (1), (2) |
8 (10/16+18+20) |
Unsupervised learning: dimensionality reduction PCA (Guest lec) |
|
|
|
Slides (1), (2) |
9 (10/23+25+27) |
Probabilistic graphical models (PGMs): naïve Bayes (NB) |
|
NB vs. LR (Ng & Jordan '01) |
|
Slides (1) |
12 (10/30, 11/1+3) |
Generative models/PGMs: Mixtures of Gaussians, clustering |
|
| HW #2 due 11/12 |
Slides (1), (2), (3) |
11 (11/6+8+10) |
Clustering, decision trees, ensembling |
|
Random Forests (Breiman '01) |
|
Slides (1), (2), (3) |
13 (11/13+15+17) |
Artificial neural networks (ANNs), reverse-mode differentiation |
|
DL Ch. 6 |
HW #3 due 12/4 |
(1), (2) |
12 (11/20) |
ANNs: tricks of the trade, variational generative modeling |
|
|
|
Slides (1) |
14 (11/27+29, 12/1) |
VAEs, violating i.i.d.: time-series |
|
DL Ch. 10 |
|
Slides (1), (2), (3) |
15 (12/4+6+8) |
ANNs: recurrence, uncertainty modeling (Guest lec) |
|
|
Final Exam/Project |
Slides (1), (2), (3) |
16 (12/13, 10:45am-1:15pm) |
Final Project Presentations |
|
|
| Slides due 12/13, 11:59pm |
| (Papers due 12/14, 8am) |
|
|
