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
Optional
 The Multivariate Gaussian Distribution [pdf]
 More on Gaussian Distribution [pdf]

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]

Lecture 13

 BiasVariance tradeoff (wrapup)
 Uniform Convergence

Class Notes
 Bias Variance Analysis [pdf]
 Statistical Learning Theory [pdf]

7/13 
Assignment 
Problem Set 2. [files][code] Due 7/27 at 11:59pm.

Week 5 
Lecture 14

 Reinforcement Learning (RL)
 Markov Decision Processes (MDP)
 Value and Policy Iterations

Class Notes
 Reinforcement Learning and Control (Sec 12) [pdf]

Lecture 15

 RL (wrapup)
 Learning MDP model
 Continuous States

Class Notes
 Reinforcement Learning and Control (Sec 34) [pdf]

Week 6 
Lecture 16

 Kmeans clustering
 Mixture of Gaussians (GMM)
 Expectation Maximization (EM)

Class Notes
 Kmeans [pdf]
 Mixture of Gaussians [pdf]
 Expectation Maximization (Sec 12, skip 2.1) [pdf]

Lecture 17

 EM (wrapup)
 Factor Analysis

Class Notes
 Expectation Maximization (Sec 3) [pdf]
 Factor Analysis [pdf]

Lecture 18

 Factor Analysis (wrapup)
 Principal Components Analysis (PCA)
 Independent Components Analysis (ICA)

Class Notes
 Principal Components Analysis [pdf]
 Independent Components Analysis [pdf]

Week 7 
Lecture 19

 Maximum Entropy and Exponential Family
 KLDivergence
 Calibration and Proper Scoring Rules

Class Notes

Lecture 20 
 Variational Inference
 EM Variants
 Variational Autoencoder

Class Notes

Lecture 21


Class Notes
 Evaluation Metrics [pptx]

7/13 
Assignment 
Problem Set 3. [files][code] Due 8/10 at 11:59pm.

Week 8 
Lecture 22

 Practical advice and tips
 Review for Finals

Class Notes

Lecture 23


Class Notes

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.
