CS 4033/4973/5033 Machine Learning Class schedule

The following is a preliminary schedule of the lectures and presentations for CS 4033/4973/5033 Machine Learning for Fall 2006. This schedule will be updated throughout the semester as we delve deeper into special topics and/or add additional topics of interest. All readings are from Machine Learning by Tom Mitchell unless otherwise noted. 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 22 (Week 1) What is Machine Learning and why should I care? Chapter 1 (Mitchell)    
Aug 24 Function approximation   Project , HW 0  
Aug 29 (Week 2) Reinforcement Learning, Exploration

Chapter 1 (Sutton & Barto)

Optional: Chapter 13 (Mitchell)

  HW 0
Aug 31 Exploration, The RL problem Chapter 2 (Sutton & Barto). Skipped the starred sections. HW 1 Written project proposals
Sep 5 (Week 3) Oral Project proposals

 

  Oral project proposals
Sep 7 The RL problem Chapter 3 (including starred section) (Sutton & Barto)    
Sep 12 (Week 4) The RL problem     HW 1
Sep 14 Oral reports, Dynamic Programming

Chapter 4 (Sutton & Barto)

HW 2 Status report 1
Sep 19 (Week 5) Temporal Difference learning Chapter 6 (Sutton & Barto). Skip the starred sections.    
Sep 21 Advanced RL topics      
Sep 26 (Week 6) Oral reports, Neural networks Chapter 4 (Mitchell) HW 3 Status report 2
Sep 28 Neural networks: backprop     HW 2
Oct 3 (Week 7) Neural networks      
Oct 5 Oral reports, Advanced topics in NN     Status report 3, HW 3
Oct 10 (Week 8) Neural net design, Overfitting

 

HW 4  
Oct 12 Evolutionary Computation: guest lecture by Dr Hougen Chapter 9 (Mitchell)    
Oct 17 (Week 9)

Oral reports, Overfitting

 

  Status report 4
Oct 19

Evolutionary computation

 

  HW 4
Oct 24 (Week 10) Learning under uncertainity Chapter 6 (Mitchell) HW 5  
Oct 26 Oral reports, Bayesian learning Optional: Chapter 14 (AIMA)   Status report 5
Oct 31 (Week 11)

Bayes Nets : what they are

 
Nov 2 Bayes nets : how to use them     HW 5
Nov 7 (Week 12) Oral reports, K-means clustering (hard EM)     Status report 6
Nov 9

Clustering, EM for mixture models

     
Nov 14 (Week 13) EM for Bayes Net structure learning   HW 6  
Nov 16 Advanced Bayesian topics    
Nov 21 (Week 14) Advanced topics by request     Preliminary writeup
Nov 23
Thanksgiving vacation (enjoy your turkey!)
Nov 28 (Week 15) Advanced topics by request     Peer reviews
Nov 30 Advanced topics by request     HW 6
Dec 5 (Week 16)
Project presentations
Final writeup (accepted to 5pm, Dec 8)
Dec 7
Project presentations
Dec 15 (Final exam period 1:30-3:30)
Project presentations