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Course Schedule (Tentative)

Lecture Date Topic Readings Notes
1 9/3 Course overview
Supervised learning
Lecture 1
Supervised Learning
2 9/5 Linear Regression (Part 1) UML 9.2
ESL 3.2
Lecture 2
3 9/10 Linear Regression (Part 2) UML 11.2
ESL 3.4, 7.10
Lecture 3
4 9/12 Linear Classification UML 12
ESL 4.1-4.2, 4.4
Lecture 4
HW1 Out
5 9/17 SVM
6 9/19 Deep networks 1
7 9/24 Deep networks 2
8 9/26 Optimization 1 HW1 Due
9 10/1 Optimization 2
Learning Theory
10 10/3 Machine learning theory 1
11 10/8 Machine learning theory 2
12 10/10 (Tentative) Model selection
Guest lecture by Akshay krishnamurthy
13 10/15 Machine learning theory 3
14 10/17
MIDTERM: IN CLASS
Ensemble methods
15 10/22 Boosting 1
16 10/24 Boosting 2
17 10/29 Random forests
18 10/31 Buffer
Interactive Learning
19 11/5 Online learning
20 11/7 Online learning
21 11/12 Multi-armed bandit learning
22 11/14 Active Learning
23 11/19 Reinforcement Learning
24 11/21 Reinforcement Learning
25 11/26 Buffer
11/28 Thanksgiving break
Additional Topics
26 12/3 Generative Adversarial Nets (GANs)
27 12/5 (Tentative) Probabilistic programing
Guest Lecture by Justin Hsu
28 12/10
FINAL EXAM