Course Schedule (Tentative)

Lecture Date Topic Readings Notes
1 1/27 Course overview Lecture 1
Supervised Learning
2 Linear Regression (Part 1) UML 9.2
ESL 3.2
Lecture 2
3 2/3 Linear Regression (Part 2) UML 11.2
ESL 3.4, 7.10
Lecture 3
4 Linear Classification
Gradient Descent
UML 12
ESL 4.1-4.2, 4.4
Lecture 4
5 2/10 Logistic Regression UML 9.3 Lecture 5
6 Support Vector Machine (Part 1)
Lagrange Duality
UML 15
ESL 12.1-12.3
Lecture 6
Scribble 6
2/16 HW 1 Due
7 2/17 Support Vector Machine (Part 2) ESL 12.1-12.3
UML 15
Lecture 7
8 Kernels ESL 5.8, 6.3, 6.7
UML 16, 20
Lecture 8
Scribble 8
9 2/24 Neural Networks DL Book 6 9
10 Optimization and Training DL Book 8
3/1 HW 2 Due
Learning Theory
11 3/2 Learning Theory (Part 1) UML 6
12 Learning Theory (Part 2) UML 11
3/9
Spring Break
13 3/16 Midterm Review UML 26
3/20 HW 3 Due
3/23
MIDTERM: IN CLASS
Ensemble methods
14 3/30 Decision Trees UML 10.2, 18
15 AdaBoost Boosting Book 3 5
16 4/6 Gradient Boosting
Bias-Variance Tradeoff
ESL 10.9 10.10
17 Bagging
Random Forest
ESL 15
4/12 HW 4 Due
Unsupervised Learning
18 4/13 PCA ESL 14.5
19 Kernel PCA
Maximum Likelihood Estimation
ESL 14.5
20 4/20 Gaussian Mixture Model
Expectation-Maximization
ESL 8.5
21 Generative Adversarial Nets
Variational Autoencoder
4/26 HW 5 Due
Interactive Learning
22 4/27 Online Learning (Part 1)
Halving and Percepron
23 Online Learning (Part 2)
Multiplicative Weights (MW)
5/4
SECOND EXAM