Machine Learning

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

Announcements Autumn 2005


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


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.


Tom Mitchell Machine Learning New York:McGraw-Hill, 1997.
Overhead Sheets from the book:


Preliminary program: (tentative)

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

  2. Decision Trees, Mitchell Ch.3 (slides)

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

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

  4. 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 !!!)

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

  6. Support Vector Machines, Francisco Borges (slides)

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

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

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

Other Literature and links