Course Schedule

Lecture	Date	Topic	Readings	Notes
1	9/3	Course overview Supervised learning
		Supervised Learning
2	9/5	Linear Regression (Part 1)	UML 9.2 ESL 3.2
3	9/10	Linear Regression (Part 2)	UML 11.2 ESL 3.4, 7.10
4	9/12	Linear Classification (Part 1) Gradient Descent	UML 12 ESL 4.1-4.2, 4.4
5	9/17	Linear Classification (Part 2) Logistic Regression	UML 9.3
6	9/19	Support Vector Machine (Part 1) Lagrange Duality	ESL 12.1-12.3 CML 7.7
7	9/24	Support Vector Machine (Part 2)	ESL 12.1-12.3 UML 15
8	9/26	Kernels and Neural Networks	ESL 5.8, 6.3, 6.7 UML 16, 20 DL Book 6	HW1 Due HW2 Out
9	10/1	Neural Networks	DL Book 9
10	10/3	Optimization and Training	DL Book 8
		Learning Theory
11	10/8	Learning Theory (Part 1)	UML 6
12	10/10	Model Selection Guest Lecture by Akshay krishnamurthy	UML 11	HW2 Due
13	10/15	Learning Theory (Part 2) Midterm Review	UML 26
	10/17	MIDTERM: IN CLASS
		Ensemble methods
14	10/22	Decision Trees	UML 10.2, 18
15	10/24	AdaBoost	Boosting Book 3 5	HW3 Out
16	10/29	Gradient Boosting Bias-Variance Tradeoff	ESL 10.9 10.10	Haiku
17	10/31	Bagging Random Forest	ESL 15
		Unsupervised Learning
18	11/5	PCA	ESL 14.5
19	11/7	Kernel PCA Maximum Likelihood Estimation	ESL 14.5	HW3 Due HW4 Out
20	11/12	Gaussian Mixture Model Expectation-Maximization	ESL 8.5
21	11/14	Variational Autoencoder
22	11/19	Generative Adversarial Nets
		Interactive Learning
23	11/21	Online Learning (Part 1) Halving and Percepron		HW4 Due HW5 Out
24	11/26	Online Learning (Part 2) Multiplicative Weights (MW)		Notes by J.K. MW Not Covered in 2nd Exam
	11/28	Thanksgiving break
25	12/3	Societal Consequences
26	12/5	Final Review
	12/10	SECOND EXAM
	12/13	HW5 Due