CS 4033/5033 Machine Learning Class schedule

Fall 2015

The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2015. 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 24 What is machine learning? What will I learn if I take this class? Types of machine learning. Mitchell Chapter 1   Pretest (in class)

Aug 26


ML Introduction, Project introduction, Machine Learning that matters ML that Matters paper Project, HW 1  
Week 2
Aug 31
Reinforcement learning
Chapter 1 (Sutton & Barto)   HW 1

Sep 2


Oral project proposals     Oral project proposals
Week 3
Sep 7
Labor Day (no class)
Sep 9 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 14 Class cancelled      
Sep 16 The RL problem   HW 2  
Week 5
Sep 21

The RL problem


  Checkpoint 1
Sep 23 Temporal Difference learning     HW 2
Week 6

Sep 28

MDP homework (3) in class

6.1-6.5, 6.8 (Sutton & Barto)

HW 3

HW 3

Sep 30
TD learning, Eligibility Traces, RL with Options, Advanced RL as needed for projects
Ch 7 (Sutton & Barto)   Checkpoint 2
Week 7

Oct 5

Introduction to supervised learning, Regression, Logistic regression, Introduction to Neural Networks

Mitchell Chapter 4 HW 4  

Oct 7

Training the network, Backprop    



Week 8
Oct 12

Advanced topics in neural nets including function approximation with RL and advanced network configurations, self organizing maps

RL/NN (see RL Book 11.1) and ML book 11.9-11.13   Checkpoint 3
Oct 14

Neural net active learning/Homework exercise

  HW 5 HW 4, HW 5
Week 9

Oct 19

Deep learning and convolution networks

Papers on D2L



Oct 21 Decision trees

Mitchell book chapter 3

The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: pages 305-317 (Section 9.2).

Week 10

Oct 26

Oral reports finished



  Checkpoint 4

Oct 28

Oral reports: Checkpoint 4



Week 11
Nov 2

Kernel methods, Support Vector Machines, Kernel trick, SVR

Papers on D2L    

Nov 4

Class cancelled     Checkpoint 5
Week 12
Nov 9 SVMs    

HW 6

Nov 11 Overfitting, Model selection, Model evaluation, Boosting, Bagging, Ensemble methods   HW 7  
Week 13
Nov 16 Random Forests, Gradient Boosted Regression Trees

Papers on D2L

Checkpoint 6 (accepted until Nov 18)
Nov 18 Introduction to Bayesian Networks

Mitchell NEW Chapter 3 (old chapter 6 but it is updated)

  HW 7
Week 14
Nov 23 Inference, Naive Bayes   HW 8  
Nov 25
Thanksgiving vacation
Week 15
Nov 30
Using Bayes Nets for Inference, Conditional Independence, Inference
  Preliminary writeup
Dec 2
K-means, Mixture models, Expectation Maximization (EM)
Mitchell Chapter 8    
Week 16
Dec 7

EM for Bayesian Networks (learning structure)

    HW 8, Peer reviews
Dec 10 Future/Applications      
Week 17
December 11 2-5pm
CS poster session: Devon Hall atrium
Dec 17, 4:30-6:30
Final writeup due