The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2009. 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 25 (Week 1)  What is machine learning? What will I learn if I take this class? Types of machine learning.  
Aug 27 Reinforcement Learning  Introduction to RL, Project introduction  Chapter 1 (Sutton & Barto)  Project  Pretest 
Sep 1 (Week 2)  Oral project proposals 
HW 1  
Sep 3  Exploration & Exploitation  2.12.3, 2.52.7 (Sutton & Barto)  
Sep 8 (Week 3)  The RL problem  Chapter 3 (Sutton & Barto) 
HW 2  HW 1, Written proposal 
Sep 10  The RL problem  
Sep 15 (Week 4)  The RL problem  HW 2  
Sep 17  Dynamic Programming  Chapter 4 (Sutton & Barto)  HW 3  
Sep 22 (Week 5)  Temporal Difference learning  6.16.5, 6.8 (Sutton & Barto) 
Checkpoint 1  
Sep 24  Eligibility Traces  Ch 7 (Sutton & Barto)  HW 3  
Sep 29 (Week 6) Supervised Learning  Nearest neighbor methods, Regression, Locally weighted regression  Section 20.4 in Russell & Norvig, two wikipedia links in previous box  HW 4  Checkpoint 2 Meeting: Handwriting, Eclairs, Tornado, Risk, Scheduling Written: Flu, Text, Turbulence, Oral: Mario 
Oct 1  Introduction to Neural Networks, , Logistic regression 
Section 20.5 in Russell & Norvig)  
Oct 6 (Week 7)  Training the network, Backprop 
HW 4  
Oct 8  Backprop  Checkpoint 3 Meeting: Text, Turbulence, Mario Written: Handwriting, Eclairs, Risk, Scheduling Oral: Flu, Tornado 

Oct 13 (Week 8)  Homework 5 in class group work on backprop and neural nets

HW 5  HW 5  
Oct 15  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 20 (Week 9) General techniques 
Function approximation with RL  HW 6  Checkpoint 4 Oral: Scheduling, Turbulence, Risk 

Oct 22  Overfitting/Model Complexity  pages 660663 of Russell & Norvig (Chapter 18)  
Oct 27 (Week 10) Graphical Models  Introduction to Bayesian Networks

14.1 ( Russell & Norvig) 18.4 (Russell & Norvig) 
HW 6  
Oct 29  Naive Bayes  Ch 8 (Mitchell sample chapter)  Checkpoint 5 Meeting: Flu, Mario, Written: Tornado, Turbulence, Risk, Scheduling Oral: Eclairs, Text 

Nov 3 (Week 11)  Bayesian Networks 
HW 7  
Nov 5  Conditional Independence  14.2  
Nov 10 (Week 12)  Exact inference  14.4 (Russell & Norvig)  Checkpoint 6 Meeting: Eclairs, Neterpillars, Turbulence, Tornado, Text, Risk, Scheduling Written: Flu, Mario 

Nov 12  Exact inference inclass exercises  HW 8 in class  HW 7, HW 8  
Nov 17 (Week 13)  Hybrid networks  14.3  
Nov 19  Mixture models, Expectation Maximization  Section 20.3 (Russell & Norvig) except for the Bayes Net part 
HW 9  
Nov 24 (Week 14)  EM for Bayesian Networks (learning structure)  Preliminary writeup  
Nov 26  Thanksgiving vacation 

Dec 1 (Week 15)  Ensemble methods  Peer reviews  
Dec 3  
Dec 8 (Week 16)  Class cancelled 
HW 9  
Dec 10  Project presentations? 
Final writeup due  
December 11, 25pm  Poster presentation: Sarkeys A & B 

Dec 17 (Final exam period 810am)  Project presentations 