Computer Science Department
Cornell University
Spring 2003


Please schedule your final project presentation using this form

05/05/2003 The final project is due May 12 at 4pm. The final exam is on May 14 (12:00-2:30pm, Phillips 219). Presentations of projects will be scheduled for May 15 and 16

04/25/2003 Solution to assignment4 is out. Due..

Please note that most questions (regarding assignments/projects/general questions about the course) should be posted to the newsgroup, not sent by email.

If the link above does not work for you, here is how to connect to the newsgroup using Microsoft Outlook Express:

  1. Go to Tools|Accounts.
  2. A dialog box will appear. Click on the Add button and select "news".
  3. Fill in your nickname, email address and use "" for the news server. A folder named "" will be created.
  4. Right click on the folder, select Property. In the "server" tab, check "the server requires me to log on". Use your netid for the account name, and your Bear Access network password for the password field.
  5. Click on Tools|newsgroup to download the list of newsgroup on the server. Add "cornell.class.cs478" to the list of subscribed newsgroup.
Additional information on how to access Cornellís news server using Bear Access and other news application can be obtained here

Time and Place


Office Hours Office
Instructor Golan Yona
Tuesday 2:00-3:00 pm
Thursday 2:00-3:00 pm
Upson 5156
Teaching Assistants Liviu Popescu
Monday 4:00 - 5:00 pm
Wednesday 4:00 - 5:00 pm
Upson 4162
Alex Ksikes
Wednesday 10:00 - 11:00 am
Upson 328
Stefan Witwicki
Monday 1:00 - 2:00 pm
Upson 328

General Information

Course Syllabus (.ps, .pdf)

Course Information (.doc) (last modified 1/22)

Academic integrity policy

Checklist (what have we covered so far):

Lecture Notes

Introduction [mostly Mitchell Ch1] (.ps, .pdf)

Concept Learning [Mitchell Ch2] (.ps , .pdf)

Decision Trees - part 1 [Mitchell Ch3, Duda/Hart/Stork Ch8] (.ps , .pdf)

Decision Trees - part 2 [Mitchell Ch3, Duda/Hart/Stork Ch8] (.ps , .pdf)

Bayesian decision theory - part 1 [Duda/Hart/Stork Ch2] (.ps , .pdf)

Bayesian decision theory - part 2 [Duda/Hart/Stork Ch2] (.ps , .pdf)

Added 03/01/2003 Bayesian decision theory (sequential inference) - part 3 (.ps , .pdf)

Bayesian learning theory - part 4 [partly from Duda/Hart/Stork Ch3] (.ps , .pdf)

Bayesian learning theory - part 5 [mostly Mitchell Ch6] (.ps , .pdf)

Bayesian learning theory - part 6 [mostly Mitchell Ch6] (.ps , .pdf)

Bayesian networks [mostly Duda/Hart/Stork chapter 2, Mitchell Ch6] (.ps , .pdf)

Hidden Markov Models - part 1 [partly Duda/Hart/Stork chapter 3] (.ps , .pdf)

Hidden Markov Models - part 2 [partly Duda/Hart/Stork chapter 3] (.ps , .pdf)

The EM algorithm (.ps , .pdf)

Nonparametric Techniques [Duda/Hart/Stork Ch4, Mitchell Ch8] (.ps , .pdf)

Linear Discriminant Functions [Duda/Hart/Stork Ch5] (.ps , .pdf)

Artificial Neural Networks I [Duda/Hart/Stork Ch6, Mitchell Ch4] (.ps , .pdf)

Artificial Neural Networks II [Duda/Hart/Stork Ch6, Mitchell Ch4] (.ps , .pdf)

Stochastic methods (genetic algorithms) [Mitchell Ch9, Duda/Hart/Stork Ch7] (.ps , .pdf)

Unsupervised learning I - clustering algorithms [Duda/Hart/Stork Ch10] (.ps , .pdf)

Unsupervised learning II - dimensionality reduction algorithms [Duda/Hart/Stork Ch10] (.ps , .pdf)

Hypothesis evaluation [mostly Mitchell Ch5] (.ps , .pdf)

Algorithm-independent Machine Learning I [Duda/Hart/Stork Ch9] (.ps , .pdf)

Algorithm-independent Machine Learning II [Duda/Hart/Stork Ch9] (.ps , .pdf)

Machine Learning - Overview (.ps , .pdf)


Note: you may work on the assignment with (one) another student, but you have to submit the assignment separately, using your own words. Acknowledge the other student with whom you worked on the assignment.

  1. Assignment #1 (ps,pdf)
    Solution of assignment 1: part A (problems 1,4) word and part B (problems 2,3,5,6,7) ps, pdf

  2. Assignment #2 (ps,pdf)
    Solution of assignment 2 ps, pdf

  3. Assignment #3 due March 14 at 4pm

  4. Assignment #4 (ps,pdf)
    Due April 1st.
    Sample input file. The pattern is of length 10. The output format should be:

    sequence 1 position 30 THEPATTERN
    sequence 2 position 12 THEPATTERN
    sequence n position 23 THEPATTERN

    likelihood ratio: 57.2

    New tests for the Gibbs sampling algorithm: report your results on these two files test1 (L=10), test2 (L=18)

    04/25/2003 Solution of assignment 4 - part1 ps, pdf, and part2 code

  5. Assignment #5 due April 15 at 11am

  6. Assignment #6 (ps, pdf) due April 23
    sample data, output format
    The MDL principle (ps,pdf)

Final Project

You are encouraged to work on the project in couples. Please register as soon as you know who is your partner.

ML Links

Demos and Supplementary Material