Tom M. Mitchell & Andrew W. Moore
School of Computer Science, Carnegie Mellon University
It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduatelevel student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem.
The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statististics and from statistical algorithmics.
Students entering the class with a preexisting working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.
Class lectures: Tuesdays & Thursdays 10:30am11:50am, Wean Hall 7500 starting on Thursday September 4th, 2003
Review sessions: Thursdays 5:00pm 6:15pm, Newell Simon Hall 1305 starting on Thursday September 11st, 2003 (details)
Instructors:
Textbook:
Course Website (this page):
Grading:
Policy on late homework:
Policy on collaboration:
Dates 
Module 1 
Instructor: Andrew Moore


Topics: (These topics will be covered during period Sep. 4 ~ Sep. 23)


Materials:


Progress:


Dates 
Module 2 
Instructor: Tom Mitchell


Topics:


Materials:


Progress:


Dates 
Module 3 
Instructor: Andrew Moore


Topics:


Materials:


Progress:


Date

Time

Place

Instructor

Topic

Sep. 8 Mon

6:30pm ~ 7:45pm

WeH 7500

Andrew Moore


Sep. 11 Thu

5:00pm ~ 6:15pm

NSH 1305

Andrew Moore


Sep. 18 Thu

4:30pm ~ 5:30pm

NSH 1305

Andrew Moore

Recent Lectures Review

Sep. 25 Thu

5:00pm ~ 6:15pm

NSH 1305

Rong Zhang

Homework 1 Help Session

Oct. 2 Thu

5:00pm ~ 6:15pm

NSH 1305

Jiayong Zhang

Homework 2 Help Session

Oct. 9 Thu

5:00pm ~ 6:15pm

NSH 1305

Andrew Moore

Midterm Review

Oct. 23 Thu

5:00pm ~ 6:15pm

NSH 1305

Andrew Moore

Review VCDim, SVM and Memorybased Learning

Oct. 30 Thu

5:00pm ~ 6:15pm

NSH 1305

Rong Zhang

Homework 4 Help Session

Nov. 6 Thu

5:00pm ~ 6:15pm

NSH 1305

Jiayong Zhang

Homework 5 Help Session

Nov. 20 Thu

5:00pm ~ 6:15pm

NSH 1305

Andrew Moore

Review GMM and Kmeans

Dec. 4 Thu

2:00pm ~ 3:00pm

NSH 3305

Andrew Moore

Extra Review Session

Dec. 7 Sun

8:00pm ~ 9:00pm

NSH 3305

Andrew Moore

Final Review

Note:
Here are some example questions for studying for the 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 apear 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.