Machine Learning

Course Number: LIX004M5
Instructor: Jörg Tiedemann, Alfa-informatica, tiedeman@let.rug.nl Off. Hours. 15:15-16 pm Mon. (after lecture)

Announcements Autumn 2005

10-08-2005: preliminary program is on-line
06-09-2005: draft of the new assignments for lab 2 (also extended deadline)
16-09-2005: lectures are moved to Academiegebouw A12 (main university building), same time.
The room for the final exam has been changed as well! It'll be in the Academiegebouw AZERN on the same day, same time!
19-09-2005: new slides for lecture 4
26-09-2005: slides for lecture 5 and assignmet 4 are on-line
30-09-2005: assignment 3 is removed!
30-09-2005: some additional information for assignment 2
04-10-2005: here are the grades for the lab reports
12-10-2005: SVM slides on-line
18-10-2005: Some links for SVMs
09-11-2005: here are the final grades for labs and the exam

Schedule

Sept 5 - Oct. 21
Lectures: ~~Harmoniegebouw 15.114~~, Academiegebouw A12 (!!!!), Mon. 13:15-15:00 pm
Labs: Fri. 9:15-11:00 am in Harmonie.12.102C. Four labs, starting Fri. Sept. 16 (2nd week, not every week!) (Students' Unix Room).
Exam: Friday, 28 Oct. 09:00 - 12:00 in the Academiegebouw ~~A901~~ AZERN

Prerequisites:

The course is open to students in Computer Science, Artificial Intelligence and Information Science. Required background consists of programming ability, elementary statistics, status as 2nd year student (or higher) in study program.

Coursework, Grades, Responsibilities

There are ~~four~~ three required labs, which count as 30% of the grade, one for 20% and 2 for 5% each. A written exam counts for the remaining 70%. Students must accept the responsibilities outlined in the general statement of intellectual responsibility for Information Science students.

Book

Tom Mitchell Machine Learning New York:McGraw-Hill, 1997.
Overhead Sheets from the book: www.cs.cmu.edu/~tom/mlbook-chapter-slides.html

Exam

The exam is open book. Books, notes and calculators may be used. There is a sample exam, taken from the Weka site.

Preliminary program: (tentative)

Organization, Inspiration, Mitchell Ch.1 &
Intro Machine Learning, Mitchell Ch.2 (slides)

Decision Trees, Mitchell Ch.3 (slides)

Lab 1 - Decision Trees (deadline: 30-09-2005)

Bayesian Learning I, Mitchell, Ch.6 (slides)

Bayesian Learning II, (H)MMs and EM, Mitchell, Ch.6 and Manning & Schütze,Ch. 9 (slides)

Lab 2 - Naive Bayes Learning (deadline: 21-10-2005 !!!)

Instance-Based Learning, Mitchell, Ch.8 (slides)

Support Vector Machines, Francisco Borges (slides)

Lab 3 - ~~Instance-based learning~~ continue working on assignment 2

Log-linear models, MaxEnt and further topics (slides)

Lab 4 - compare classifiers (deadline: 28-10-2005)