Time and Location:
Monday, Wednesday 4:305:50pm, Bishop Auditorium
Class Videos:
Current quarter's class videos are available here for SCPD students and here for nonSCPD students.
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We may update the course materiels. Please check for the latest version before lectures.
Event  Date  Description  Materials and Assignments  

Lecture 1  4/1  Introduction and Basic Concepts 
Class Notes: Introduction [pdf] 

A0  4/3  Problem Set 0 [pdf] [solution]. Out 4/1. Due 4/10. Submission instructions.  
Lecture 2  4/3  Supervised Learning Setup. Linear Regression.  Class Notes  
Section  4/5 
Discussion Section: Linear Algebra [Notes] 

Lecture 3  4/8 
Weighted Least Squares. Logistic Regression. Netwon's Method Perceptron. Exponential Family. Generalized Linear Models. 
Class Notes  
Lecture 4  4/10  
A1  4/10 
Problem Set 1 [zip]. Out 4/10. Due 4/24. Submission instructions. 

Section  4/12  Discussion Section: Probability [Notes][Slides]  
Lecture 5  4/15  Gaussian Discriminant Analysis  Class Notes  
Lecture 6  4/17 
Naive Bayes. Laplace Smoothing. Kernel Methods. 

Section  4/19  Discussion Section: Python [slides]  
Lecture 7  4/22  SVM. Kernels.  Class Notes  
Lecture 8  4/24  Neural Network.  Class Notes  
A2  4/24 
Problem Set 2 [zip]. Out 4/24. Due 5/8. Submission instructions. 

Section  4/26  Discussion Section: Learning Theory [pdf]  
Project  4/26  Project proposal due at 11:59pm.  
Lecture 9  4/29  Neural Network.  Class Notes  
Lecture 10  5/1 
Bias/ Variance. Regularization. Feature/ Model selection. 
Class Notes  
Section  5/3  Discussion Section: Evaluation Metrics [Slides]  
Lecture 11  5/6  Practical Advice for ML projects 
Class Notes


Lecture 12  5/8  Kmeans. Mixture of Gaussians. Expectation Maximization.  Class Notes  
A3  5/8 
Problem Set 3 [zip]. Out 5/8. Due 5/22. Submission instructions. 

Section  5/10  Discussion Section: Midterm Review [pdf]  
Lecture 13  5/13  GMM(EM). Variational Autoencoders. 
Class Notes


Lecture 14  5/15  Principal Component Analysis. Independent Component Analysis.  Class Notes  
Midterm  5/15  We will have an inclass midterm from 7pm to 10pm. Logistics. SCPD Logistics. Practice Midterm.  
Lecture 15  5/20  MDPs. Bellman Equations. Value iteration and policy iteration 
Class Notes


Lecture 16  5/22  Value function approximation.  Class Notes  
A4  5/22 
Problem Set 4 [zip]. Out 5/22. Due 6/5. Submission instructions. 

Section  5/24  Discussion Section: Convolutional Neural Nets [pdf]  
Project  5/24  Project milestones due 5/24 at 11:59pm.  
Lecture 18  5/29  Policy search. REINFORCE. 
Class Notes


Section  5/31  Discussion Section: Gaussian Processes [pdf]  
Lecture 19  6/3  Other settings of RL, Imitation learning, Adversarial machine learning  Class Notes  
Lecture 20  6/5  Course Review and Wrap up  Class Notes  
Project  6/11 
Project poster PDF and project recording (remote SCPD only) due at 11:59 pm Submission instructions. 

Project  6/12  Poster presentations from 3:306:30pm. Venue and details to be announced.  
Project  6/12  Final writeup due at 6:30pm (no late days).  
Section Notes


Other Resources


Supplementary Notes 