3/29 
Lecture 1


Class Notes

3/31 
Lecture 2

 Supervised learning setup. LMS.

Class Notes
 Supervised Learning[pdf](Sections 13)
 Live Lecture Notes (draft)[pdf]

3/31 
Assignment 
Problem Set 0 released.

4/2 
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]

4/5 
Lecture 3

 Weighted Least Squares. Logistic regression. Newton's Method.

Class Notes
 Supervised Learning [pdf] (Sections 4, 5, and 7)
 Live Lecture Notes (draft)[pdf]

4/7 
Lecture 4

 Dataset split; Exponential family. Generalized Linear Models.

Class Notes
 Supervised Learning [pdf] (Sections 6, 8, and 9)
 Live Lecture Notes (draft)[pdf]

4/7 
Assignment 
Problem Set 1 will be released. Due Wednesday, 4/21 at 11:59pm

4/9 
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]

4/12 
Lecture 5

 Gaussian discriminant analysis. Naive Bayes.

Class Notes
 Generative Algorithms [pdf] (Section 1)
 Live Lecture Notes [pdf]

4/14 
Lecture 6

 Naive Bayes, Laplace Smoothing.

Class Notes
 Naive Bayes and Laplace Smoothing [pdf] (Section 2)
 Live Lecture Notes [pdf]

4/16 
Project 
Project proposal due 4/16 at 11:59pm. 
4/16 
Section 3

 Friday TA Lecture: Python/Numpy Tutorial.

Notes

4/19 
Lecture 7


Class Notes
 Kernel Methods [pdf]
 Live Lecture Notes [pdf]

4/21 
Lecture 8


Class Notes
 Deep Learning [pdf]
 Live Lecture Notes [pdf]

4/21 
Assignment 
Problem Set 2 will be released. Due Wednesday, 5/5 at 11:59pm

4/23 
Section 4

 Friday TA Lecture: Evaluation Metrics.

Notes
 Friday Section Slides [pdf]

4/26 
Lecture 9

 Neural Networks 2. Backpropagation.

Class Notes
 Deep Learning [pdf]
 Live Lecture Notes [pdf]

4/28 
Lecture 10

 Bias  Variance. Regularization. Feature / Model selection.

Class Notes
 Regularization and Model Selection [pdf]
 Some Calculations from Bias Variance (Addendum) [pdf]
 BiasVariance and Error Analysis (Addendum) [pdf]
 Live Lecture Notes [pdf]

4/30 
Section 5

 Friday TA Lecture: Deep Learning (ConvNets).

Notes
 Friday Section Slides [pdf, ppt]

5/3 
Lecture 11

 KMeans. GMM (non EM). Expectation Maximization.

Class Notes
 Unsupervised Learning, kmeans clustering. [pdf]
 Mixture of Gaussians [pdf]
 The EM Algorithm [pdf]
 Live Lecture Notes (draft)[pdf]

5/5 
Lecture 12

 GMM (EM). Factor Analysis.

Class Notes
 Lagrange Multipliers Review [pdf]
 Factor Analysis [pdf]
 Live Lecture Notes (draft)[pdf]
 Addendum Notes[pdf]

5/5 
Assignment 
Problem Set 3 will be released. Due Wednesday, 5/19 at 11:59pm

5/7 
Project 
Project milestones due 5/7 at 11:59pm. 
5/7 
Section 6

 Friday TA Lecture: Midterm Review.


5/10 
Lecture 13


Class Notes
 Principal Components Analysis [pdf]
 Independent Component Analysis [pdf]
 Live Lecture Notes (draft)[pdf]

5/12 
Lecture 14

 Weak supervised / unsupervised learning.

Class Notes
 Introduction to weak supervision [slides]
 ICA and weak supervision [draft]

5/13 
Midterm 
The midterm details TBD.

5/14 
Section 7

 Friday TA Lecture: Decision Trees + Boosting.

Notes

5/17 
Lecture 15



5/19 
Lecture 16

 ML Advice. Selfsupervised learning (Language Models & Image Models).


5/19 
Assignment 
Problem Set 4 will be released. Due Wednesday, 5/26 at 11:59pm

5/21 
Section 8

 Friday TA Lecture: On Critiques of ML.


5/24 
Lecture 17

 Basic concepts in RL, value iteration, policy iteration.

Class Notes
 Basic RL concepts, value iterations, policy iteration [pdf] (Sections 1 and 2)

5/26 
Lecture 18

 Modelbased RL, value function approximator.

Class Notes
 Modelbased RL and value function approximation [pdf] (Sections 3 and 4)

5/28 
Section 9

 Friday TA Lecture: Learning Theory.


6/2 
Lecture 19



6/2 
Project 
Project final report + poster (optional) due 6/2 at 11:59pm. 
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.
