CS494/594: Schedule/Readings/Notes

CS494/594: Projects in Machine Learning


Spring 2006

Machine Learning home page Syllabus Schedule/Readings Project Assignments Resources

Subject to change. Check back frequently for updates.
    Last updated: April 27, 2006

Date Topics Assigned Readings
Thurs. 1/12 Course Introduction
Intro. to Learning

Lecture slides

Ch. 1
Tues. 1/17 Reinforcement Learning
   Elements of RL problem
Ch. 16
Thurs. 1/19 Reinforcement Learning (con't.)
   K-armed bandit
   State-action pairs
   Defining learning rate
   Exploration vs. exploitation
Ch. 16 (con't.)

Class handout (Ch. 21 from Russell and Norvig)

Tues. 1/24 Reinforcement Learning (con't.)
Ch. 16 (con't.)

Project 1 assigned; due Feb. 12

Thurs. 1/26 Player/Stage tutorial
    (to be used in Project 1;
    taught by Michael Bailey)
Player/Stage Getting Started Guide (UTK-specific)

Player/Stage Documentation (Public-domain website)

Tues. 1/31 Reinforcement Learning (con't.)
    Case Studies
    Practical implementation issues

Lecture slides

"Getting Reinforcement Learning to Work on Real Robots", by Smart and Kaelbling, Artificial Intelligence, 55 (2-3), 311-365, 1991.

"Learning to Coordinate Behaviors", by Maes and Brooks, Proc. of 8th Nat'l. Conf. on Artificial Intelligence (AAAI-90), AAAI Press/MIT Press, pgs. 796-802, 1990.

Improving Elevator Performance Using Reinforcement Learning, In Touretzky, et al (eds.), Advances in Neural Information Processing Systems: Proc. of the 1995 Conference, MIT Press, pgs. 1017-1023, 1996.

Thurs. 2/2 Neural Networks
Ch. 11

"The Basic Ideas in Neural Networks", by Rumelhart et al., Communications of the ACM, 37(3): 87-92, 1994.

Applet links (just to play around with):
  Neural net function learning applet
  Optical character recognition applet

Tues. 2/7 Neural Networks (con't.)
Ch. 11 (con't.)

Applet link (just to play around with):
  Perceptron learning rule applet

Thurs. 2/9 Neural Networks (con't.)
    Multi-layer feedforward NNets
    Back Propagation
Ch. 11 (con't.)

Project 2 assigned; due Mar. 7

Tues. 2/14 Neural Networks (con't.)
    K-fold cross-validation
    Case studies:
        Face Recognition
    Design and performance issues
Ch. 14.2.1 (pg. 331) [See also top of today's Mitchell handout]

Class handout (face recognition case study from Mitchell)

"Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets", by Gorman and Sejnowski, Neural Networks, Vol. 1, pgs. 75-89, 1988.

Ch. 11 (con't.)

Thurs. 2/16 In-class design competition #1 (in teams):
Using neural networks or reinforcement learning for the Paddle Ball task (competing for extra credit points)
Lecture notes on neural nets
Tues. 2/21 Review of in-class design exercise
Review of Project #1
Introduction to Genetic Algorithms
Feedback: In Class Design #1 Exercise

Feedback: Project #1

Class handout (Ch. 9 from Mitchell on Genetic Algorithms)
Thurs. 2/23 Genetic Algorithms (con't.)
   Representation of hypotheses
   Genetic operators
   Fitness selection methods
     DNF satisfiability
     Traveling Salesman Problem
Applet link (just to play around with):
  Traveling Salesman Problem Genetic Algorithm applet
   (go towards bottom of page)
Tues. 2/28 Genetic Algorithms (con't.)
Genetic Programming
   Block-stacking example
Thurs. 3/2 Genetic Programming (con't.)
    Boolean 11-multiplexer example
    Design details
    Artificial ant example
Tues. 3/7 Genetic Programming (con't.)
    Symbolic regression example
Project 3 assigned;
  Part I due at 23:59:59 on Wed. Mar. 15
  Part II due at 08:00 on Thurs. April 6

Class handout (GP examples from Koza text)

Class handout (GP details -- parts of Ch. 6 of Koza text)

Videos: evolutionary computation (for designing robots)
   (see also "results" link at end of page)

Thurs. 3/9 Introduction to density estimation
Parametric Learning
Ch. 1.2.4, Ch. 4
Tues. 3/14 In-class design competition #2 (in teams)
Using genetic programming for the pole-balancing task (competing for extra credit points)
Thurs. 3/16 No class; instructor on travel

(Work on those projects!!)

Tues. 3/21 Spring Break; no class  
Thurs. 3/23 Spring Break; no class  
Tues. 3/28 Multivariate parametric learning Ch. 5.1-5.4
Thurs. 3/30 Multivariate classification Ch. 5.5-5.7
Tues. 4/4 Dimensionality reduction   Subset Selection
  Principal Components Analysis (PCA)
Ch. 6.1-6.3

Applet link (just to play around with):   on PCA

Thurs. 4/6 (Briefly) Factor Analysis
K-Means Clustering
Project 4 assigned; due April 27
Ch. 6.4
Ch. 7.3
Tues. 4/11 Fuzzy C-Means Clustering
   Case Study: Gesture Recognition
Hierarchical Clustering
Handout: X. Li paper (unpublished) on gesture recognition using Fuzzy C-Means clustering

Ch. 7.7

Thurs. 4/13 Self-Organizing Maps (Overview)

Lecture slides on SOMs

Class handout (SOMs applied to EEGs):
"Self-Organizing Map in Recognition of Topographic Patterns of EEG Spectra", by Joutsiniemi, Kaski, and Larsen, IEEE Transactions on Biomedical Engineering, Vol. 42, No. 11, pgs. 1062-1068

Ch. 12.2.3

Applet link (just to play around with):   on SOMs

Tues. 4/18 Lazy Learning (Overview)
  (i.e., Instance-based and case-based learning)
Tutorial slides on Instance Based learning

Tutorial slides on Case Based Resasoning

Ch. 8
Thurs. 4/20 Anomaly Detection (Overview)
  (applied to biosurveillance case study)

Tutorial slides anomaly detection, applied to biosurveillance

Tues. 4/25 Introduction/Overview of Project 5

Combining Multiple Learners (Overview)

Project 5 (Poster) assigned; to be presented in Poster Session during final exam period (May 4)
Hints on how to prepare a poster

Ch. 15

Thurs. 4/27 Combining Multiple Learners (con't.)

"Final Quiz" (doesn't count! just for extra credit)

Thurs. 5/4, 7:15PM - 9:15PM Poster Session (during scheduled final exam time period)