This table will be updated regularly through the quarter to reflect what was covered, along with corresponding readings and notes.

Date	Event	Description	Materials and Assignments
Week 1
9/21	Lecture 1	Introduction.	Class Notes
9/21	Assignment	Problem Set 0 released.
9/23	Lecture 2	Supervised learning setup. LMS.	Class Notes Supervised Learning [pdf] (Sections 1-3)
9/23	Assignment	Problem Set 1 will be released. Due Thursday, 10/7 at 11:59pm
Week 2
9/28	Lecture 3	Weighted Least Squares. Logistic regression. Newton's Method.	Class Notes Supervised Learning [pdf] (Sections 4, 5, and 7)
9/30	Lecture 4	Dataset split; Exponential family. Generalized Linear Models.	Class Notes Supervised Learning [pdf] (Sections 6, 8, and 9) Live Lecture Notes [pdf]
10/1	Section 1	Friday TA Lecture: Linear Algebra Review.	Notes Linear Algebra Review and Reference [pdf] Linear Algebra, Multivariable Calculus, and Modern Applications (Stanford Math 51 course text) [pdf] Friday Section Slides [pdf]
10/1	Project	Project proposal due 10/1 at 11:59pm.
Week 3
10/5	Lecture 5	Gaussian discriminant analysis. Naive Bayes.	Class Notes Generative Algorithms [pdf] (Section 1) Live Lecture Notes [pdf]
10/7	Lecture 6	Naive Bayes, Laplace Smoothing.	Class Notes Naive Bayes and Laplace Smoothing [pdf] (Section 2) Live Lecture Notes [pdf]
10/7	Assignment	Problem Set 2 will be released. Due Thursday, 10/21 at 11:59pm
10/8	Section 2	Friday TA Lecture: Probability Theory Review.	Notes Probability Theory Review [pdf] The Multivariate Gaussian Distribution [pdf] More on Gaussian Distributions [pdf] Friday Section Slides [pdf]
Week 4
10/12	Lecture 7	Kernels. SVM.	Class Notes Kernel Methods [pdf] Live Lecture Notes [pdf]
10/14	Lecture 8	Neural Networks 1.	Class Notes Deep Learning [pdf] Live Lecture Notes [pdf]
10/15	Section 3	Friday TA Lecture: Python/Numpy Tutorial.	Notes Python Review Code Friday Section Slides [pdf]
Week 5
10/19	Lecture 9	Neural Networks 2. Backpropagation.	Class Notes Deep Learning [pdf] Live Lecture Notes [pdf]
10/21	Lecture 10	Bias - Variance. Regularization. Feature / Model selection.	Class Notes Regularization and Model Selection [pdf] Some Calculations from Bias Variance (Addendum) [pdf] Bias-Variance and Error Analysis (Addendum) [pdf] Lecture slides: bias and variance [pdf] Lecture slides: lasso regression [pdf] Lecture slides: ridge regression [pdf]
10/21	Assignment	Problem Set 3 will be released. Due Thursday, 11/4 at 11:59pm
10/22	Section 4	Friday TA Lecture: Midterm Review.	Notes Friday Section Slides [pdf]
10/22	Project	Project milestones due 10/22 at 11:59pm.
Week 6
10/26	Lecture 11	Decision Trees. Boosting	Class Notes Decision trees slides [pdf] Overfitting decision trees slides [pdf] The EM Algorithm slides [pdf]
10/28	Lecture 12	K-Means. GMM (non EM). Expectation Maximization. Factor Analysis.	Class Notes Unsupervised Learning, k-means clustering. [pdf] Mixture of Gaussians [pdf] The EM Algorithm [pdf] Factor Analysis [pdf] Kmeans slides [pdf]
10/28	Midterm	For midterm details, please see this post on Ed
10/29	Section 5	Friday TA Lecture: Evaluation Metrics.	Notes Friday Section Slides [pdf]
Week 7
11/2	Lecture 13	EM. GMM.	Class Notes The EM Algorithm [pdf] Principal Components Analysis [pdf] Independent Component Analysis [pdf] EM Slides [pdf] GMM Slides [pdf]
11/4	Lecture 14	PCA. Types of learning.	Class Notes PCA [slides, annotated] Types of learning [slides, annotated]
11/4	Assignment	Problem Set 4 will be released. Due Saturday, 11/20 at 11:59pm
11/5	Section 6	Friday TA Lecture: Deep Learning (ConvNets).	Notes Deep Learning [slides]
Week 8
11/9	Lecture 15	ML Advice	Class Notes
11/11	Lecture 16	Unsupervised learning. Reinforcement learning.	Class Notes
11/12	Section 7	Friday TA Lecture: Ensembling Techniques	Notes Ensembling Techniques [pdf]
Week 9
11/16	Lecture 17	Basic concepts in RL, value iteration, policy iteration.	Class Notes Basic RL concepts, value iterations, policy iteration [pdf] (Sections 1 and 2) Live Lecture Notes [pdf]
11/18	Lecture 18	Model-based RL, value function approximator.	Class Notes Model-based RL and value function approximation [pdf] (Sections 3 and 4) Live Lecture Notes (Spring 2021) [pdf]
11/19	Section 8	Friday TA Lecture: On Critiques of ML.	Class Notes
Thanksgiving Break
Week 10
11/30	Lecture 19	Fairness, algorithmic bias, explainability, privacy
12/2	Lecture 20	Fairness, algorithmic bias, explainability, privacy
12/2	Project	Project final report + poster due 12/2 at 11:59pm.
12/3	Section 9	Friday TA Lecture: Learning Theory.	Class Notes Learning theory [pdf]
Other Resources All lecture videos can be accessed through Canvas. Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. Previous projects: A list of last year's final projects can be found here. 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. 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. Machine learning study guides tailored to CS 229 by Afshine Amidi and Shervine Amidi. The Matrix Cookbook: quick reference for matrix identities, approximations, relations, etc.