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 (Part 1)	DL Book 6	Lecture 9
10		Neural Networks (Part 2)	DL Book 8	Lecture 10
	3/1	HW 2 Due
		Learning Theory
11	3/2	Neural Networks (Part 3) Convolutional Neural Networks	DL Book 8	Lecture 11
12		Learning Theory (Part 1)		Lecture 12
	3/9	Spring Break Course moving online due to COVID-19
13		Learninig Theory (Part 2)	UML 4, 6	Lecture 13
14		Learninig Theory (Part 3)	UML 4, 6, 26	Lecture 14
	3/29	HW 3 Due
		Ensemble methods
15		AdaBoost (Part 1) Decision Trees	Boosting Book 3, 5 UML 10.2, 18	Lecture 15
16		AdaBoost (Part 2)	Boosting Book 3, 5	Lecture 16
17		Gradient Boosting	ESL 10.9 10.10	Lecture 17
18		Bagging Random Forest	ESL 15	Lecture 18
	4/15	HW 4 Due
		Unsupervised Learning
19		PCA	ESL 14.5	Lecture 19
20		Kernel PCA Maximum Likelihood Estimation	ESL 14.5	Lecture 20
21		Gaussian Mixture Model Expectation-Maximization	ESL 8.5	Lecture 21
22-23		Variational Autoencoder		Lecture 22-23
24		Generative Adversarial Nets		Lecture 24
		Interactive Learning
25		Online Learning (Part 1) Halving and Percepron		Lecture 25
26		Online Learning (Part 2) Multiplicative Weights (MW)		Note by J.K.
	5/4	HW 5 Due
	5/8	Takehome Due