CS229 Syllabus Spring 2022

	A	B	C	D	E	F
1			Date	Lecture Topics	Deliverables	Notes
2	Week 1	Lecture 1	3/28/2022	Introduction		Slides
3		Lecture 2	3/30/2022	Supervised learning setup. LMS.		Draft, Template, Notes; Section 1 of Main Notes
4		TA Lecture 1	4/1/2022	Linear Algebra Review		Notes; Slides; Annotated Slides
5	Week 2	Lecture 3	4/4/2022	Weighted Least Squares. Logistic regression. Newton's Method		Draft, Template, Notes; Section 2 of Main Notes
6		Lecture 4	4/6/2022	Exponential family. Generalized Linear Models.		Draft; Section 3 of Main Notes
7			4/6/2022		Problem Set 0 (Due at 11:59 pm PT - Ungraded)
8		TA Lecture 2	4/8/2022	Probability Review		Notes; Slides
9	Week 3	Lecture 5	4/11/2022	Gaussian discriminant analysis. Naive Bayes.		Section 4.1 of Main Notes
10		Lecture 6	4/13/2022	Naive Bayes, Laplace Smoothing.		Section 4.2 of Main Notes
11			4/15/2022		Final Project Proposal (Due at 11:59 pm PT)
12		TA Lecture 3	4/15/2022	Python/Numpy		Slides; Materials
13	Week 4	Lecture 7	4/18/2022	Kernels		Section 5 of Main Notes
14		Lecture 8	4/20/2022	Neural Networks 1		Draft, Template, Notes; Section 7.1 & 7.2 of Main Notes
15			4/20/2022		Problem Set 1 (Due at 11:59 pm PT)
16		TA Lecture 4	4/22/2022	Evaluation Metrics		Slides
17	Week 5	Lecture 9	4/25/2022	Neural Networks 2 (backprop)		Section 7.3 of Main Notes
18		Lecture 10	4/27/2022	Bias - Variance. Regularization.		Section 8 of Main Notes
19		TA Lecture 5	4/29/2022	Deep Learning (Conv Nets)		Slides
20	Week 6	Lecture 11	5/2/2022	Feature / Model selection. ML Advice.		Section 9 of Main Notes, slides (only subset of first 40 pages are covered in the lecture)
21		Lecture 12	5/4/2022	K-Means. GMM (non EM). Expectation Maximization.		Draft; Section 10, 11.1, 11.2 of Main Notes
22			5/4/2022		Problem Set 2 (Due at 11:59 pm PT)
23			5/6/2022		Final Project Milestone (Due at 11:59 pm PT)
24		TA Lecture 6	5/6/2022	Midterm Review		Slides
25	Week 7	Lecture 13	5/9/2022	GMM (EM)		Draft; Section 11.2-11.4 of Main Notes
26		Lecture 14	5/11/2022	Factor Analysis/PCA		Draft; Section 12&13 of Main Notes
27			5/12/2022		MIDTERM (CEMEX Auditorium, 6 pm - 9 pm PT)
28				No TA Lecture (Midterm Week)
29	Week 8	Lecture 15	5/16/2022	PCA/ICA		Draft; Draft; Section 13 of Main Notes
30		Lecture 16	5/18/2022	Self-supervised learning		Draft; Section 14 of Main Notes
31			5/18/2022		Problem Set 3 (Due at 11:59 pm PT)
32			5/20/2022	No TA Lecture
33	Week 9	Lecture 17	5/23/2022	basic concepts in RL,value iteration, policy iteration		Draft; Section 15 of Main Notes
34		Lecture 18	5/25/2022	Societal impact of ML(Guest lecture by Prof. James Zou)
35		TA Lecture 7	5/27/2022	Decision Trees + Boosting		Slides (Boosting), Slides (Decision Trees)
36	Week 10	Lecture 19	5/30/2022	MEMORIAL DAY. NO LECTURE.
37		Lecture 20	6/1/2022	Model-based RL, value function approximator
38		TA Lecture 8	6/3/2022	Learning Theory		Notes
39
40			6/1/2022		Problem Set 4 (Due at 11:59 pm PT)
41			6/6/2022		Final Project Report (Due at 11:59 pm PT)
42			6/7/2022		Final Project Poster Session (3:30 pm - 6:30 pm PT)
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100