In order to facilitate the
interaction between students and TAs in
this large class, we have split the class into four groups, and
assigned each group to one of the four TAs. This TA is your "first
point of contact" for courserelated issues. Please send any questions,
concerns, etc. to your point of contact:
Last names starting with AD: contact Daniel (neill@cs)
Last names starting with ELe: contact Kaustav (kaustav@cs)
Last names starting with LiP: contact Derek (dhoiem@cs)
Last names starting with QZ: contact Zhenzhen (woomy@cs)
Homeworks will be done individually: each student must hand in their own answers. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. You also must indicate on each homework with whom you collaborated. The final project may be completed by small teams.
If you feel that we have made an error in grading your homework, please turn in your homework with a written explanation to Sharon, and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.
Module 
Material covered 
Online material and links

Dates and Instructor 
Module
1:
Basics (2 Lectures) 

Jan 10: Function Approximation and Version Spaces Jan 12: Point Estimation& Linear Regression Link to the regression applet: http://www.mste.uiuc.edu/users/exner/java.f/leastsquares/ 
Jan 10:
Tom Mitchell Jan 12: Carlos Guestrin 
Module
2:
Linear models (2 Lectures) 

Jan
19: NaiveBayes Classifiers Jan 24: Logistic Regression Draft Chapter on NaiveBayes and Logistic Regression 
Jan 17:
MLK Day, no classes Jan 19: Tom Mitchell Jan 24: Tom Mitchell 
Module
3: Nonlinear models Model selection (5 Lectures) 

Jan 26: Logistic Regression and Bias/Variance Jan 31: Neural Networks Feb 2: Neural Networks/Cross Validation Feb 9: Decision Trees/MDL/Boosting Boosting Paper Adaboost Applet Feb 14: InstanceBased Learning KNN Applet 
Jan
26: Tom Mitchell Jan 31: Tom Mitchell Feb 2: Carlos Guestrin Feb 7: Midmini break  no classes Feb 9: Tom Mitchell Feb 14: Carlos Guestrin 
Module
4:
Marginbased approaches (2 Lectures) 

Feb 16: SVM slides SVM Applets Hearst 1998: High Level Presentation Burges 1998: Detailed Tutorial Burges (cleaner ps version) Feb 21: Kernels for SVM slides SVM Applets 
Feb 16: Carlos Guestrin Feb 21: Carlos Guestrin 
Module
5:
Learning theory (3 Lectures) 

Feb 23:
PAC learning Feb 28: PAC/VC Slides Learning with Kernels Example of More Complex Bounds (Zhang ML 2002) Mar 2: Mistake bounds MidReview Comments 
Feb 23: Tom
Mitchell Feb 28: Carlos Guestrin Mar 2: Tom and Carlos 
Spring break

March 711


Midterm Exam 
All material thus far 
March 14


Module 6: Structured models (4 Lectures) 

Mar 16: Mar 21: Mar 23:
Mar 28:

Mar
16: Carlos Guestrin Mar 21: Carlos Guestrin Mar 23: Carlos Guestrin Mar 28: Carlos Guestrin 
Module
7:
Unsupervised and semisupervised learning (4 Lectures) 

Mar 30:
Apr 4:

Mar
30: Tom
Mitchell Apr 4: Tom Mitchell Apr 6: Tom Mitchell Apr 11: Tom Mitchell 
Module
8:
Invited lectures in advanced topics (5 Lectures) 

Apr 13:
Apr 27: 
Apr 13: Carlos
Guestrin Apr 18: Ron Parr Apr 20: Ron Parr Apr 25: Daniel Neill Apr 27: Tom Mitchell 
Final Exam 
All material thus far 
May 9, 1:004:00pm

Date

Time

Place

Instructor

Topic

Tue
Jan. 11

5:00pm
~ 6:30pm

NSH
3305

Daniel

Review
of Basic Probability Concepts

Thurs
Jan. 20

5:00pm
~ 6:30pm

Doherty
1212

Zhenzhen


Thurs
Jan. 27

5:00pm
~ 6:30pm

Doherty
1212

Kaustav

Naive
Bayes and Logistic Regression

Thurs
Feb. 3

5:00pm
~ 6:30pm

Doherty
1212

Derek


Thurs
Feb. 10

5:00pm
~ 6:30pm

Doherty
1212

Daniel

Decision
Trees / Boosting

Thurs
Feb. 17

5:00pm
~ 6:30pm

Doherty
1212

Derek

HW2
/ KNN / Exam Review (Mid 2003)

Thurs
Feb. 24

5:00pm
~ 6:30pm

Doherty
1212

Kaustav

SVM

Thurs
Mar. 3

5:00pm
~ 6:30pm

Doherty
1212

Zhenzhen

PAC
Learning / Midterm Review

Thurs
Mar. 10

5:00pm
~ 6:30pm

Doherty
1212

Spring
Break

Spring
Break

Thurs
Mar. 17

5:00pm
~ 6:30pm

Doherty
1212

Zhenzhen


Thurs
Mar. 24

5:00pm
~ 6:30pm

Doherty
1212

TBD

TBD

Thurs
Mar. 31

5:00pm
~ 6:30pm

Doherty
1212

TBD

TBD

Thurs
Apr. 7

5:00pm
~ 6:30pm

Doherty
1212

TBD

TBD

Thurs
Apr. 14

5:00pm
~ 6:30pm

Doherty
1212

TBD

TBD

Thurs
Apr. 21

5:00pm
~ 6:30pm

Doherty
1212

Zhenzhen


Thurs
Apr. 28

5:00pm
~ 6:30pm

Doherty
1212

TBD

TBD

Here are some example questions here for studying for the midterm/final. Note that these are exams from earlier years, and contain some topics that will not appear in this year's final. And some topics will appear this year that do not appear in the following examples.
Feel free to use the slides and materials available online here. Please email the instructors with any corrections or improvements. Additional slides and software are available at the Machine Learning textbook homepage and at Andrew Moore's tutorials page.