The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2011. This specific order of each topic will depend on the class projects and interest. This will be finalized in the first week. The schedule will be updated throughout the semester as we delve deeper into special topics and/or add additional topics of interest. The project deadlines are in red and will not change, even if other parts of the schedule change. Major new topics are noted in blue.
Date  Topic  Assigned Reading  Assigned  Due 
Aug 23 (Week 1)  What is machine learning? What will I learn if I take this class? Types of machine learning.  
Aug 25 Reinforcement Learning 
Project introduction, Introduction to RL  Chapter 1 (Sutton & Barto)  Project  Pretest 
Aug 30 (Week 2)  Oral project proposals 

Sep 1  Intro to RL, Exploration & Exploitation  2.12.3, 2.52.7 (Sutton & Barto)  
Sep 6 (Week 3)  The RL problem  Chapter 3 (Sutton & Barto) 
Homework 1  Written project proposal, teams formed 
Sep 8  The RL problem  
Sep 13 (Week 4)  The RL problem  Homework 2  Homework 1  
Sep 15  Dynamic Programming  Chapter 4 (Sutton & Barto)  
Sep 20 (Week 5)  Temporal Difference learning  6.16.5, 6.8 (Sutton & Barto) 
Checkpoint 1  
Sep 22  Eligibility Traces, RL with Options  Ch 7 (Sutton & Barto)  Homework 3  Homework 2 
Sep 27 (Week 6) Supervised Learning 
Nearest neighbor methods, Regression, Locally weighted regression  Ch 7 (sections 7.17.2 except * sections, 7.4 except *) from Murphy book  Checkpoint 2 

Sep 29  Logistic regression, Introduction to Neural Networks 
Readings on D2L  Homework 3  
Oct 4 (Week 7)  Training the network, Backprop 

Oct 6  Backprop  Checkpoint 3: group meetings with Dr McGovern


Oct 11 (Week 8)  Backprop  in class homework on neural nets

Homework 4  
Oct 13  Class cancelled 

Oct 18 (Week 9) General techniques 
Oral status reports: checkpoint 4 
Checkpoint 4 

Oct 20  Finish oral status report (checkpoint 4), Advanced topics in neural nets including function approximation with RL  RL/NN (see RL Book 11.1), also 8.18.4 in RL book  
Oct 25 (Week 10) Ensemble methods 
Function approximation with RL,

Readings on D2L 

Oct 27 SVMs

Boosting, Bagging, Ensemble methods 
Checkpoint 5


Nov 1 (Week 11)  Overfitting, Introduction to Support Vector Machines 
SVM paper (D2L)  Homework 5  
Nov 3 Graphical Models 
Support vector machines, kernels  
Nov 8 (Week 12)  Support Vector Regression  SVR paper (D2L)  Checkpoint 6


Nov 10  Introduction to Bayesian Networks  Chapter readings on D2L  Homework 6  Homework 5 
Nov 15 (Week 13)  Using Bayes Nets for Inference  
Nov 17  Conditional Independence, Inference 


Nov 22 (Week 14)  Naive Bayes  Preliminary writeup, Homework 6  
Nov 24  Thanksgiving vacation 

Nov 29 (Week 15)  Class cancelled  Homework 7  Peer reviews  
Dec 1  Mixture models, Expectation Maximization (EM)  
Dec 6 (Week 16)  EM for Bayesian Networks (learning structure) 

Dec 8  Hybrid networks  Homework 7  
December 9, 25pm  CS poster session: Devon Hall atrium 

Dec 16, 1:303:30  Final writeup due 