Up: Dana's Home page

Machine Learning

Instructor: Dana H. Ballard

TA: Juhyun Lee TA: Robert Lindquist Text: Pattern Recognition and Machine Learning by Christopher Bishop

Overview

Course Credit:
Credit will be based on ten assignments (75%), a midterm (10%), and a final (15%). The assignments will generally be due Friday, midnight of every week. Exceptions will be made on a per/assignment basis. Most assignments will require computer programming in Matlab.
Late Policy:
1 day: -15%
2 days: -30%
3 days: -50%
4 days: No credit
If extenuating circumstances will make it difficult for you to complete a project on time, contact the TA to work things out.
Academic dishonesty:
Students caught plagiarizing will fail the course. On some projects, we will use MOSS to analyze code. Avoid plagiarism by carefully citing all your sources. If you use a specific short code snippet found on a web-page, mention that fact in a comment. If someone else told you how to solve a tricky part of an assignment, give them credit too. Do not copy code from other students. However, if you did and cited them for it, I suppose that you would not fail for plagiarism; you simply would not get credit for the assignment. Even so, don't do it.
Homework submission:
You will submit homework using the turnin program available on CS lab machines. If you don't have a CS account, Get One. Instructions are available by typing man turnin. Essentially, you will type "turnin --submit impjdi hw# projectFile.m report.pdf" where '#' is the number of the homework you are submitting and projectFile.m is your code and report.pdf is your report. We generally prefer that you not compress or tar the files since that adds another step in the already annoying process of grading.
You can check your submission by making a new directory and typing "turnin --verify impjdi hw#". This will copy back to you the files that you have submitted. This way, you can make sure that everything is in order (and you did not accidentally submit a teacher-provided file instead of the file with your changes).

Week Date Topic Reading Assignment Slides etc
027 AugIntroduction Bishop Ch 1  
1 1 Sep Probability Theory Bishop 1 EM EMLec-slides CoinPaper
3 SepProb Thy  
2 8 SepLinear Algebra Classnotes Eigenfaces  
10 SepDynamical Systems Classnotes  
315 SepInformation Thy 1 Bishop 1.6 DeMixing via ICA  
17 SepInformation Thy 2 Classnotes  
422 SepOptimization Thy Classnotes CAR SIMULATION  
24 SepOptimization Thy 2 Bishop Appendix D and E  
529 SepPerceptronsBishop 4.1, 4.2   
1 OctBackpropagation Classnotes  
6 6 OctSVMs e.g XOR ; Bishop 325-345 Neural Nets  
8 OctLearning Thy SVM Tutorial  
713 OctDecision Trees Practice Mid Term
15 OctReview
820 OctMidTerm MidTerm Answers
22 OctBayes Nets Intro Research Paper SVM HW Bayes Net Slides
927 OctBayes Net Algorithm KohonenHW and
Report instructions
29 OctSOM KohonenNotes
10 3 NovMarkov models NewNotes
5 NovHidden Markov models
1110 NovReinforcement Learning RL PPT RL Homework
12 NovRL2 RL Notes
1217 NovGenetic Algorithms GA Notes Homework: GA
19 NovGenetic Algorithms
1324 NovGames Homework: Games
Hauert Paper
14 1 DecGames
Evaluations
Zhu:games
3 Dec2nd Exam Front , Back , Summary