CS229 Fall 2023-24 Syllabus.xlsx

	A	B	C	D	E	F	G	H	I
1			Date	Lecture Topics	Deliverables	Notes	Slides
2	Week 1	Lecture 1	9/27/2023	Introduction
3		TA Lecture 1	9/29/2023	Linear Algebra Review		Notes	Slides
4	Week 2	Lecture 2	10/2/2023	Supervised learning setup. LMS.		Sections 1.1, 1.2 of main notes
5		Lecture 3	10/4/2023	Weighted Least Squares. Logistic regression. Newton's Method		Sections 1.3, 1.4, 2,1, 2.3 of main notes
6			10/4/2023		Problem Set 0 (Due at 11:59 pm PT - Ungraded)
7		TA Lecture 2	10/6/2023	Probability Review		Notes	Slides
8	Week 3	Lecture 4	10/9/2023	Dataset split; Exponential family. Generalized Linear Models.		Section 2.2 and Chapter 3 of main notes
9			10/9/2023		Final Project Proposal (Due at 11:59 pm PT)	CS229 Final Project Fall 2022-23
10		Lecture 5	10/11/2023	Bias-variance tradeoff, regularization		Sections 8.1, 9.1, 9.3	Bias/variance slides Ridge regression slides Lasso regression slides Bias/variance annotated Ridge annotated
11			10/11/2023		Problem Set 1 (Due at 11:59 pm PT)
12		TA Lecture 3	10/13/2023	Python/Numpy		jupyter notebook	slides
13	Week 4	Lecture 6	10/16/2023	Decision trees		Decision trees & ensemble learning	Boosting slides Decision Trees slides Decision Trees annotated Decision Trees Overfitting Lasso annotated
14		Lecture 7	10/18/2023	Boosting		Decision trees & ensemble learning
15		TA Lecture 4	10/20/2023	Evaluation Metrics			slides
16	Week 5	Lecture 8	10/23/2023	Gaussian discriminant analysis. Naive Bayes.		Section 4.1, 4.2 of main notes
17		Lecture 9	10/25/2023	Kernels; SVM		Chapter 5
18			10/27/2023		Problem Set 2 (Due at 11:59 pm PT)
19		TA Lecture 5	10/27/2023	Midterm Review			Slides
20			10/27/2023		Final Project Milestone (Due at 11:59 pm PT)	CS229 Final Project Fall 2022-23
21	Week 6	Lecture 10	10/30/2023	K-Means. GMM. Expectation Maximization.		Section 10, 11 of main notes	K-means slides EM slides PCA slides K-means annotated EM annotated PCA annotated	GMM slides	GMM annotated
22		Lecture 11	11/1/2023	ML Advice			ML advice
23			11/3/2023		MIDTERM: HEWLET200 (Last name A-L) & STLC111 (Last name M-Z) , 6 pm - 9 pm PT
24				No TA Lecture (Midterm Week)
25	Week 7	Lecture 12	11/6/2023	Neural Networks 1		Sections 7.1, 7.2
26		Lecture 13	11/8/2023	Neural Networks 2 (backprop)		Section 7.3
27			11/10/2023		Problem Set 3 (Due at 11:59 pm PT)
28		TA Lecture 6	11/10/2023	Deep Learning (Convnets)			Slides
29	Week 8	Lecture 14	11/13/2023	Basic concepts in RL, value iteration, policy iteration.
30		Lecture 15	11/15/2023	Model-based RL, value function approximator
31		TA Lecture 7	11/17/2023	GANs
32	Week 9	Lecture 16	11/27/2023	EM, PCA
33		Lecture 17	11/29/2023	Other learning settings. Large language models & foundation models			Learning + foundation models
34			12/1/2023		Problem Set 4 (Due at 11:59 pm PT)
35	Week 10	Lecture 18	12/4/2023	fairness, algorithmic bias, explainability, privacy			fairness fairness annotated
36		Lecture 19	12/6/2023	fairness, algorithmic bias, explainability, privacy			privacy privacy annotated explainability explainability annotated
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39			12/8/2023		Final Project Report (Due at 11:59 pm PT)	CS229 Final Project Fall 2022-23
40			12/13/2023		Final Project Poster Session (3:30 pm - 6:30 pm PT)
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43	Other Resources
44	(Hover over each cell for hyperlinks)
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46	All lecture videos can be accessed through Canvas.
47	Advice on applying machine learning: Slides from Andrew Ng's lecture on getting machine learning algorithms to work in practice can be found here.
48	Previous projects: Projects from previous years can be found in the “Final Projects” doc on the home page.
49	Data: Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences NeurIPS (all old NeurIPS papers are online) and ICML. Some other related conferences include UAI, AAAI, IJCAI.
50	Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one.
51	Machine learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi.
52	The Matrix Cookbook: Quick reference for matrix identities, approximations, relations, etc.
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