Time and Location:
Monday, Wednesday 9:3010:50am, Bishop 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  9/24  Introduction and Basic Concepts  Class Notes  
A0  9/24  Problem Set 0 [pdf]. Out 9/24. Due 10/3. Submission instructions.  
Supervised learning (6 classes)  
Lecture 2  9/26  Supervised Learning Setup. Linear Regression.  
Section  9/28 
Discussion Section: Linear Algebra [Notes] 

Lecture 3  10/1 
Weighted Least Squares. Logistic Regression. Netwon's Method Perceptron. Exponential Family. Generalized Linear Models. 
Class Notes  
Lecture 4  10/3  
A1  10/3 
Problem Set 1 [zip]. Out 10/3. Due 10/17. Submission instructions. 

Section  10/5  Discussion Section: Probability[Notes][Slides]  
Lecture 5  10/8  Gaussian Discriminant Analysis. Naive Bayes.  Class Notes  
Lecture 6  10/10 
Laplace Smoothing. Support Vector Machines. 

Section  10/12  Discussion Section: Vectorization[Slides][kNN][Logistic Regression][Softmax Regression][images][labels]  
Lecture 7  10/15  Support Vector Machines. Kernels.  
Learning theory (2 classes)  
Lecture 8  10/17  BiasVariance tradeoff. Regularization and model/feature selection.  Class Notes  
A2  10/17 
Problem Set 2 [zip]. Out 10/17. Due 10/31. Submission instructions. 

Section  10/19  Discussion Section: Convex Optimization  
Project  10/19  Project proposal due at 11:59pm.  
Lecture 9  10/22  Tree Ensembles.  Class Notes  
Deep Learning (2 classes)  
Lecture 10  10/24 
Neural Networks. Backpropagation. 
Class Notes  
Lecture 11  10/29  Error Analysis. Practical Advice on structuring ML projects.  
Section  10/26  Discussion Section: Evaluation Metrics [Slides]  
Unsupervised learning (5 classes)  
Lecture 12  10/31  KMeans. Expectation Maximization.  Class Notes  
Lecture 13  11/5  EM. Gaussian Mixture Model.  
Lecture 14  11/7  Factor Analysis.  
Lecture 15  11/12  Principal Component Analysis. Independent Component Analysis.  
Lecture 16  11/14  MDPs. Bellman Equations.  
Section  11/2  Discussion Section: Midterm Review  
A3  10/31 
Problem Set 3 [pdf]. Out 10/31. Due 11/14. Submission instructions. 

Midterm  11/7  We will have a takehome midterm. All details are posted on Piazza.  
Section  11/16  Discussion Section: Deep Learning Methods  
Project  11/16  Project milestones due 11/16 at 11:59pm.  
Reinforcement learning and control (4 classes)  
Lecture 17  11/26  Value Iteration and Policy Iteration. LQR. LQG.  Class Notes  
Lecture 18  11/28  QLearning. Value function approximation.  
Lecture 19  12/3  Policy Search. REINFORCE. POMDPs.  
Lecture 20  12/5  Optional topic. Wrapup.  
A4  11/14 
Problem Set 4 [pdf]. Out 11/14. Due 12/5. Submission instructions. 

Section  11/30  Discussion Section: Deep Learning Platform  
Project  12/10 
Project poster PDF and project recording (some teams) due at 11:59 pm Submission instructions. 

Project  12/11  Poster presentations from 8:3011:30am. Venue and details to be announced.  
Project  12/13  Final writeup due at 11:59pm (no late days).  
Supplementary Notes  
Section Notes


Other Resources
