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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 (Part 1)
Gradient Descent
UML 12
ESL 4.1-4.2, 4.4
Lecture 4
HW1 Out
5 9/17 Linear Classification (Part 2)
Logistic Regression
UML 9.3 Lecture 5
6 9/19 Support Vector Machine (Part 1)
Lagrange Duality
ESL 12.1-12.3
CML 7.7
Lecture 6
7 9/24 Support Vector Machine (Part 2) ESL 12.1-12.3
UML 15
Lecture 7
8 9/26 Kernels and Neural Networks ESL 5.8, 6.3, 6.7
UML 16, 20
DL Book 6
Lecture 8
HW1 Due
HW2 Out
9 10/1 Neural Networks DL Book 9 Lecture 9
10 10/3 Optimization and Training DL Book 8 Lecture 10
Learning Theory
11 10/8 Learning Theory (Part 1) UML 6 Lecture 11
12 10/10 Model Selection
Guest Lecture by Akshay krishnamurthy
UML 11 Lecture 12
HW2 Due
13 10/15 Learning Theory (Part 2)
Midterm Review
UML 26 Lecture 13
10/17
MIDTERM: IN CLASS
Ensemble methods
14 10/22 Decision Trees UML 10.2, 18 Lecture 14
15 10/24 AdaBoost Boosting Book 3 5 Lecture 15
HW3 Out
16 10/29 Gradient Boosting
Bias-Variance Tradeoff
ESL 10.9 10.10 Lecture 16
Haiku
17 10/31 Bagging
Random Forest
ESL 15 Lecture 17
Unsupervised Learning
18 11/5 PCA Lecture 18
19 11/7 Kernel PCA
Maximum Likelihood Estimation
Lecture 19
HW3 Due
HW4 Out
20 11/12 VAE
21 11/14 GAN
Interactive Learning
22 11/19 Online Learning
23 11/21 Bandits Learning HW4 Due
HW5 Out
24 11/26 Reinforcement Learning
11/28 Thanksgiving break
25 12/3 Reinforcement Learning
26 12/5 (Tentative) Probabilistic programing
Guest Lecture by Justin Hsu
HW5 Due
27 12/10
FINAL EXAM