Machine Learning

Course Number: LIX004M5
Instructor: John Nerbonne, Alfa-informatica, Off. Hours. 15:15-16 pm Mon. (after lecture)
Lab Leader: Francisco Borges, Alfa-informatica,

Announcements Fall 2004


Sept 6 - Oct. 18
Lectures: Oude Boteringestr. 13, room 102, Mon. 13:15-15:00 pm. (was Harmonie 13.342)
Labs: Fri. 9:15-11 am in Harmonie.12.102C. Four labs, starting Fri. Sept. 24 (3rd week) (Students' Unix Room). See Francisco's Borges's Machine Learning Labs web site for a preview.
Exam: Friday, 29 Oct. 09:00 - 12:00 in the Academiegebouw A901


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 required labs, which count as 20% of the grade. A written exam counts for the remaining 80%. 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:

Other Literature


Preliminary program: (tentative)

  1. Organization, Inspiration Mitchell Ch.1
    Intro Machine Learning Mitchell Ch.2 (Symbolic Theory Revision, Version Spaces)
    Exercises from Mitchell, Chap. 2

  2. Decision Trees Mitchell Ch.3
    Exercises from Mitchell, Chap. 3

  3. Bayesian Learning Mitchell, Ch.6;
    Exercise from Mitchell, Chap. 6

    Lab 1 - Decision Trees

  4. Instance-Based Learning Mitchell, Ch.8
    Example: Daelemans et al. on Dutch Diminutives

    Lab 2 - Naive Bayes Learning

  5. Rule Induction Mitchell, Ch.10
    Rules Induced from Decision Trees
    Cohen's paper on RIPPER (available here)

    Lab 3 - to be announced (IBL or RIPPER)

  6. Support Vector Machines Borges project.

    Lab 4 - t.b.a. (IBL, RIPPER or SVM)

  7. Estimation Maximization Mitchell, Ch.6; Rabin?
    Example: Hidden Markov Models

  8. Local Research on ML