Date	Topic	Subtopic	Section	Readings	Assignment
			0. Math and Code Review
Jan. 27 (M)	Overview	Intro		2.1, 2.2.1	HW1 (Regression) released
Jan. 29 (W)	Regression	Linear Regression		2.3-2.5, 2.6.1, 2.7.1
Jan. 31 (F)
			1. Linear Regression
Feb. 3 (M)		Prob. Regression		2.6.2, 2.6.3
Feb. 5 (W)	Classification	Linear Classification		3.1-3.5
Feb. 7 (F)					HW1 (Regression) due
			2. Prob. Regression, Classification
Feb. 10 (M)		Prob. Classification		3.6	HW2 (Classification) released
Feb. 12 (W)	Model Selection	Model Selection - Frequentist		2.7, 2.8
Feb. 14 (F)
			3. Model Selection
Feb. 17 (M)	President's Day	No class
Feb. 19 (W)		Model Selection - Bayesian		2.8, 2.9
Feb. 21 (F)					HW2 (Classification) due
			4. Bayesian Approaches, Neural Networks
Feb. 24 (M)	Function Class	Neural Net 1		4.1-4.4, 4.6	HW3 (Bayesian Methods, NN, and Practical Supervised Learning) released
Feb. 26 (W)		Neural Net 2		4.4
Feb. 28 (F)
			Midterm 1 Review
Mar. 2 (M)	Objective	Support Vector Machine 1		5.1-5.3
Mar. 4 (W)		Support Vector Machine 2		5.4
Mar. 6 (F)					HW3 (Bayesian Methods, NN, and Practical Supervised Learning) due
			5. Margin-Based Classification, SVMs
Mar. 9 (M)		Ethics in ML
Mar. 11 (W)		Midterm 1
Mar. 13 (F)
Mar. 16 (M)	Spring Break	No class			HW4 (SVM, Clustering, and Ethics) released
Mar. 18 (W)		No class
Mar. 20 (F)
			6. Clustering
Mar. 23 (M)	Unsupervised Learning	Clustering		6
Mar. 25 (W)		Mixture Models		9.1-9.5
Mar. 27 (F)					HW4 (SVM, Clustering, and Ethics) due
			7. Mixture Models, EM, PCA
Mar. 30 (M)		Principal Component Analysis		7	HW5 (Mixtures, EM, and Graphical Models) released
Apr. 1 (W)	PGMs	Topic Models		9.6
Apr. 3 (F)
			8. Bayesian Networks
Apr. 6 (M)		Graphical Models		8
Apr. 8 (W)		Inference for BNs
Apr. 10 (F)					HW5 (Mixtures, EM, and Graphical Models) due
			9. Variable Elimination, HMMs, and Kalman Filters
Apr. 13 (M)		Hidden Markov Models		10	HW6 (Inference in Graphical Models, MDPs) released
Apr. 15 (W)		Markov Decision Processes		SB 3.1-3.6, SB 4.1-4.4 *
Apr. 17 (F)
			10. Markov Decision Processes and Reinforcement Learning
Apr. 20 (M)	Reinforcement Learning	Reinforcement Learning 1		SB 1.1-1.4, SB 6.1-6.5 *
Apr. 22 (W)		Reinforcement Learning 2
Apr. 26 (S)					HW6 (Inference in Graphical Models, MDPs) due
Apr. 27 (M)		Final Lecture - Interpretability
Apr. 29 (W)					Independent Assignment

*SB refers to Sutton and Barto 2018, Reinforcement Learning: An Introduction.