Machine Learning

Course Number: LIX004M5
Instructor: Jörg Tiedemann, Alfa-informatica, j.tiedemann@let.rug.nl

Announcements Autumn 2008

Schedule

Sept 1 - Oct. 24
Lectures: A901, Mon. 09:15-11:00
Labs: Fri. 9:15-11:00 in Harmonie.12.107.
Exam: Friday, 24 Oct. 09:00 - 12:00 in the Academiegebouw 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 will be 3 obligatory assignments that have to be passed by the students by handing in 3 short reports according to the deadlines specified on this website. Passing these assignments is a prerequiste for the final exam at the end of the course. There will also be a final assignment which will count as 50% of the grade. This assignment is evaluated by means of a written report and an oral presentation during the last lecture of the course. The final written exam counts for the remaining 50% of the grade. 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.

Preliminary program

date type topic literature/info
1 Sep Lecture Organization, Introduction Ch. 1 & 2, slides
5 Sep Lab 1 evaluation, first assignment, introduction: final projects Ch. 5, slides, deadline first assignment:8 Sep
8 Sep Lecture Decision Trees Ch. 3, slides
12 Sep Lab 2 select & get started with final project mail your selection to Cagri
15 Sep Lecture Instance-Based Learning Ch. 8, slides
19 Sep Lab 3 second assignment deadline: 22 Sep
22 Sep Lecture Bayesian Learning + EM Ch. 6, slides
26 Sep Lab 4 work on final project e-mail possible questions to Cagri and/or me
29 Sep Lecture Sequential data & Markov Models slides, Manning & Schütze,Ch. 9, Bilmes: What HMMs can do
3 Oct Lab 5 third assignment deadline: 6 Oct
6 Oct Lecture Reinforcement Learning Ch. 13, slides
10 Oct Lab work on final assignment deadline report: 3 Nov
13 Oct Presentations Student Presentations (two sessions!) 9:15 - 11:00 (A901) AND 11:15 - 13:00 (A2)
24 Oct Exam   9:00-12:00, AZERN, sample exam (Note: some topics might be not relevant for this year's course)

Guidelines for writing lab reports


About WEKA

Some information about WEKA and how to use it (old):

Other Literature and links