Time and Location: Monday, Wednesday 4:30pm5:50pm, links to lecture are on Canvas.
Class Videos: Current quarter's class videos are available here for SCPD students and
here for nonSCPD students.
Note: This is being updated for Spring 2020. The dates are subject to change as we figure out deadlines. Please check back soon.
Week  Event  Date  Description  Materials 

Week 1  Lecture 1  4/6  Introduction and Basic Concepts  Slides 
Lecture 2  4/8  Supervised Learning Setup. Linear Regression.  Class Notes  
Assignment  4/8  Problem Set 0. Due 4/15 at 11:59pm.  
Section 1  4/10  Friday Lecture: Linear Algebra. 
Notes


Week 2  Lecture 3  4/13 
Weighted Least Squares. Logistic Regression. Netwon's Method Perceptron. Exponential Family. Generalized Linear Models. 
Class Notes

Lecture 4  4/15 
Class Notes


Assignment  4/15  Problem Set 1. Due 4/29 at 11:59pm.  
Section 2  4/17  Friday Lecture: Probability 
Notes


Week 3  Lecture 5  4/20  Gaussian Discriminant Analysis. Naive Bayes. Laplace Smoothing.  Class Notes 
Lecture 6  4/22 
Laplace Smoothing. Support Vector Machines. 
Class Notes  
Section 3  4/24  Friday Lecture: Python and Numpy 
Notes


Project  4/24  Project proposal due 4/24 at 11:59pm.  
Week 4  Lecture 7  4/27  Support Vector Machines. Kernels. 
Class Notes

Lecture 8  4/29  Neural Networks  1 
Class Notes


Assignment  4/29 
Problem Set 2. Due 5/13 at 11:59pm.


Section 4  5/1  Friday Lecture: Evaluation Metrics 
Notes


Week 5  Lecture 9  5/4  Neural Networks  2 
Class Notes

Lecture 10  5/6  Bias  Variance. Regularization. Feature / Model selection.  Class Notes  
Section 5  5/8  Friday Lecture: Deep Learning 
Notes


Week 6  Lecture 11  5/11  KMeans. GMM (non EM). Expectation Maximization.  Class Notes 
Lecture 12  5/13  Expectation Maximization (continued) 
Class Notes


Assignment  5/13 
Problem Set 3. Due 5/27 at 11:59pm. 

Section 6  5/15  Friday Lecture: Midterm Review 
Class Notes


Project  5/15  Project milestones due 5/15 at 11:59pm.  
Week 7  Lecture 13  5/18  Factor Analysis. 
Class Notes

Midterm  5/20  See details at Piazza post  
Lecture 14  5/20  Principal and Independent Component Analysis. 
Class Notes


Week 8  Lecture 15  5/25  Memorial Day, no lecture.  
Lecture 16  5/27  Weak Supervision 
Class Notes


Assignment  5/27 
Problem Set 4. Due 6/10 at 11:59pm (no late days). 

Week 9  Lecture 17  6/1  Markov Decision Process. Value Iteration and Policy Iteration. QLearning. Value function approximation. 
Class Notes

Lecture 18  6/3  Reinforcement Learning continued  
Week 10 (Last Week of class)  Lecture 19  6/8  Policy search. Reinforce. POMDPs. 
Class Notes

Lecture 20  6/10  Recap, Fairness, Adversarial 
Class Notes


Project  6/10  Poster PDF and video presentation. Due 6/10 at 11:59pm (no late days).  
Project  6/10  Project final report. Due 6/10 at 11:59pm (no late days).  
Supplementary Notes Optional Topics  
Other Resources
