Machine Learning

Course Number: LIX004M5
Instructor: Jörg Tiedemann, Alfa-informatica,

Announcements Autumn 2007


Sept 3 - Oct. 26
Lectures: Academiegebouw, van der Leeuwzaal, Mon. 09:15-11:00 pm
Labs: Fri. 9:15-11:00 am in Harmonie.12.107.
Exam: Friday, 26 Oct. 09:00 - 12:00 in the Academiegebouw AZERN


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.


Tom Mitchell Machine Learning New York:McGraw-Hill, 1997.

Preliminary program

date type topic literature/info
3 Sep Lecture Organization, Introduction Ch. 1 & 2, slides
7 Sep Lab 1 evaluation, first assignment, introduction: final projects Ch. 5, slides, deadline first assignment: 14 Sep
10 Sep Lecture Decision Trees Ch. 3, slides
14 Sep Lab 2 select & get started with final project mail your selection to Matthijs
17 Sep Lecture Instance-Based Learning Ch. 8, slides
21 Sep Lab 3 decision trees & IBL second assignment, deadline: 28 Sep
24 Sep Lecture Bayesian Learning Ch. 6, slides
28 Sep Lab 4 work on final project e-mail possible questions to Matthijs and/or me
1 Oct Lecture Rule Induction & Reinforcement Learning Ch. 10 & 13 + article on RIPPER, slides
5 Oct Lab 5 Naive Bayes & Ripper third assignment, deadline: 12 Oct
8 Oct Lecture Reinforcement Learning, Genetic Algorithms & Summary Ch. 13 + Ch. 9, slides
12 Oct Lab work on final assignment deadline report: 2 Nov
15 Oct Presentations Student Presentations 9:00 (!) - 11:00 (van der Leeuwzaal), 11:15-13:00 (H (13)13 0309)
26 Oct Exam   sample exam (Note: some topics such as HMM's are not relevant for this year)

Guidelines for writing lab reports

About WEKA

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

Other Literature and links