CS 4033/5033 Machine Learning Class schedule

Fall 2014

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

2.1-2.3, 2.5-2.7 (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

6.1-6.5, 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

K-means, 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 11.9-11.13   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, 2-5pm
CS poster session: Devon Hall atrium
Dec 9, 4:30-6:30
Final writeup due