6/22 
Lecture 0

 Introduction and Logistics

Class Notes

6/22 
Assignment 
Problem Set 0. [files] Due 6/29 at 11:59pm.

Week 1 
Lecture 1


Class Notes
 Linear Algebra (section 13) [pdf]
 Additional Linear Algebra Note [pdf]

Lecture 2

 Review of Matrix Calculus
 Review of Probability

Class Notes
 Linear Algebra (section 4) [pdf]
 Probability Theory [pdf]
 Probability Theory Slides [pdf]

Lecture 3

 Review of Probability and Statistics

Class Notes

6/29 
Assignment 
Problem Set 1. [files][code] Due 7/13 at 11:59pm.

Week 2 
Lecture 4

 Linear Regression
 Gradient Descent (GD), Stochastic Gradient Descent (SGD)
 Normal Equations
 Probabilistic Interpretation
 Maximum Likelihood Estimation (MLE)

Class Notes
 Supervised Learning (section 13) [pdf]

Lecture 5

 Perceptron
 Logistic Regression
 Newton's Method

Class Notes
 Supervised Learning (section 57) [pdf]

Lecture 6

 Exponential Family
 Generalized Linear Models (GLM)

Class Notes
 Supervised Learning (section 89) [pdf]

Week 3 
Lecture 7

 Gaussian Discriminant Analysis (GDA)
 Naive Bayes
 Laplace Smoothing

Class Notes
 Generative Algorithms [pdf]

Lecture 8

 Kernel Methods
 Support Vector Machine

Class Notes
 Kernel Methods and SVM [pdf]

Lecture 9


Class Notes

Week 4 
Lecture 10

 Neural Networks and Deep Learning

Class Notes
 Deep Learning (skip Sec 3.3) [pdf]
Optional

Lecture 11



Lecture 12

 Bias and Variance
 Regularization, Bayesian Interpretation
 Model Selection

Class Notes
 Regularization and Model Selection [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.
