The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2014. 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.
Date  Topic  Assigned Reading  Assigned today  Due today 
Week 1 

Aug 18  What is machine learning? What will I learn if I take this class? Types of machine learning.  Chapter 1 (ML book)  Pretest (in class)  
Aug 20

ML Introduction, Project introduction, Machine Learning that matters  ML that Matters paper  Project, HW 1  
Week 2 

Aug 25  Reinforcement learning 
Chapter 1 (Sutton & Barto)  HW 1  
Aug 27

Oral project proposals  Oral project proposals  
Week 3 

Sep 1  Labor Day (no class)


Sep 3  Exploration & Exploitation, The RL Problem  2.12.3, 2.52.7 (Sutton & Barto) Chapter 3 (Sutton & Barto) 
Written project proposal, teams formed  
Week 4 

Sep 8  The RL problem  HW 2  
Sep 10  The RL problem  
Week 5 

Sep 15  RL problem and Dynamic Programming 
Chapter 4 (Sutton & Barto) 
Checkpoint 1  
Sep 17  Temporal Difference learning  HW 2  
Week 6 

Sep 22  MDP homework (3) in class 
6.16.5, 6.8 (Sutton & Barto) 
HW 3  HW 3 
Sep 24  No class
but checkpoint 2 still due 
Checkpoint 2  
Week 7 

Sep 29 
TD learning, Eligibility Traces, RL with Options, Advanced RL as needed for projects 
Ch 7 (Sutton & Barto)  HW 4  
Oct 1 
Introduction to supervised learning, Regression, Logistic regression, Introduction to Neural Networks 


Week 8 

Oct 6  Training the network, Backprop

Checkpoint 3  
Oct 8  Advanced topics in neural nets including function approximation with RL and advanced network configurations, Introduction to Bayesian Networks 
HW 4  
Week 9 

Oct 13 
Neural net active learning/Homework exercise 

HW 5  HW 5 
Oct 15  Oral reports: Checkpoint 4  Checkpoint 4  
Week 10 

Oct 20 
Oral reports finished



Oct 22 
Using Bayes Nets for Inference, Conditional Independence, Inference 


Week 11 

Oct 27  Inference, Naive Bayes 
HW6  Checkpoint 5  
Oct 29 
Kmeans, Mixture models, Expectation Maximization (EM)  
Week 12 

Nov 3  EM for Bayesian Networks (learning structure)  HW 7  HW 6 

Nov 5  Boosting, Bagging, Ensemble methods  RL/NN (see RL Book 11.1) and ML book 11.911.13  Checkpoint 6  
Week 13 

Nov 10  Dr McGovern was out sick 


Nov 12  Overfitting, Model selection, c Kernel methods, Support Vector Machines, Kernel trick, SVR 

HW 8  
Week 14 

Nov 17  Snow day  
Nov 19  Kernel methods, Support Vector Machines, Kernel trick, SVR (see email) 
HW 7  
Week 15 

Nov 24  SVMs and SVR 
Preliminary writeup  
Nov 26  Thanksgiving vacation 

Week 16 

Dec 1  PCA/Dimensionality reduction, Deep learning, Applications 
HW 8, Peer reviews  
Dec 3  Future/Applications  
Week 17 

December 5, 25pm  CS poster session: Devon Hall atrium 

Dec 9, 4:306:30  Final writeup due 