Time and Location:
Monday, Wednesday 9:3010:50am, NVIDIA Auditorium
Class Videos:
Current quarter's class videos are available here for SCPD students and here for nonSCPD students.
Event  Date  Description  Materials and Assignments  

Introduction (1 class)  
Lecture 1  9/25  1. Basic concepts  Class Notes  
A0  9/25  Problem Set 0 [pdf]. Submission instructions.  
Supervised learning (5 classes)  
Lecture 2  9/27  1. Supervised learning setup. LMS.  
Section  9/29  Discussion Section: Linear Algebra  Discussion Section: Linear Algebra [Notes] 

Lecture 3  10/2  2. Logistic regression. Perceptron. Exponential family.  
Lecture 4  10/4  
A1  10/4 
Problem Set 1 [pdf]. Out 10/4. Due 10/18. Submission instructions. 

Section  10/6  Discussion Section: Probability  Discussion Section: Probability[Notes][Slides]  
Lecture 5  10/9  3. Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.  Class Notes  
Lecture 6  10/11  4. Support vector machines.  Class Notes  
Section  10/13  Discussion Section: Vectorization  Discussion Section: Vectorization[Slides][kNN][Logistic Regression][Softmax Regression][images][labels]  
Practice ML advice (2 classes)  
Lecture 7  10/16 
1. Bias/variance tradeoff 2. Model selection and feature selection 
Class Notes  
Lecture 8  10/18  3. Evaluating and debugging learning algorithms 4. Practical advice on structuring an ML project 

A2  10/18 
Problem Set 2 [pdf]. Out 10/18. Due 11/1. Submission instructions. 

Section  10/20  Discussion Section: Convex Optimization  Discussion Section: Convex Optimization  
Project  10/20  Project proposal due at 11:59pm.  
Deep Learning (2 classes)  
Lecture 9  10/23 
1. NN architecture 2. Forward/Back propagation 
Class Notes  
Lecture 10  10/25  3. Vectorization 4. Other optimization tricks. 

Section  10/27  Discussion Section: Evaluation Metrics  Discussion Section: Evaluation Metrics [Slides]  
Unsupervised learning (5 classes)  
Lecture 11  10/30 
1. Clustering. Kmeans. 2. EM. Mixture of Gaussians. 3. Factor analysis. 4. PCA (Principal components analysis). 5. ICA (Independent components analysis). 
Class Notes Problem Set 3 Out 11/1. Due 11/15.  
Lecture 12  11/1  
Lecture 13  11/6  
Lecture 14  11/8  
Lecture 15  11/13  
Section  11/3  Discussion Section: MidtermReview  Discussion Section: MidtermReview  
A3  11/1 
Problem Set 3 [pdf]. Out 11/1. Due 11/15. Submission instructions. 

Midterm  11/8  The midterm is openbook/opennotes/open laptop (no internet). It will take place on Wednesday, November 8, 2017 from 69 PM. The course staff will announce exam venue and material covered closer to the midterm date.  
Section  11/17  Discussion Section: Deep Learning Methods  Discussion Section: Deep Learning Methods  
Project  11/20  Project milestones due 11/20 at 11:59pm.  
Reinforcement learning and control (4 classes)  
Lecture 16  11/15 
1. MDPs. Bellman equations. 2. Value iteration and policy iteration. 3. Linear quadratic regulation (LQR). LQG. 4. Qlearning. Value function approximation. 5. Policy search. Reinforce. POMDPs. 
Class Notes Problem Set 4 Out 11/15. Due 12/6.  
Lecture 17  11/27  
Lecture 18  11/29  
Lecture 19  12/4  
Section  12/1  Discussion Section: Deep Learning Platform  Discussion Section: Deep Learning Platform  
Lecture 20  12/6  Adversarial machine learning  TBD  
Project  12/12  Poster presentations from 8:3011:30am. Venue and details to be announced.  
Project  12/15  Final writeup due at 11:59pm (no late days).  
Supplementary Notes  
Section Notes


Other Resources
