Machine Learning

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

Announcements Autumn 2006

Schedule

Sept 4 - Oct. 20
Lectures: Academiegebouw A12, Mon. 13:15-15:00 pm
Labs: Fri. 9:15-11:00 am in Harmonie.12.102C. Four labs, starting Fri. Sept. 15 (2nd week, not every week!) (Students' Unix Room).
Exam: Friday, 27 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 are four required labs, which count as 50% of the grade, 10% each except the last one (20%). A written exam counts for the remaining 50%. 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

Guidelines for writing lab reports


Preliminary program

  1. Organization, Inspiration &
    Intro Machine Learning, Mitchell Ch.1, Ch.5
    slides

  2. Inductive learning as search with bias, Mitchell Ch.2
    Decision Trees, Mitchell Ch.3
    slides

    Lab 1 - Decision Trees (deadline: 21-09-2006)

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

    Lab 2 - Instance-Based Learning (deadline: 28-09-2006)

  4. Bayesian learning, Mitchell Ch.6
    (slides)

    Lab 3 - Learner comparison/combination (deadline: 05-10-2006)

  5. Sequential data & Markov Models
    Manning & Schütze,Ch. 9,
    Bilmes: What HMMs can do

    (slides)

    Lab 4 - Markov Models (deadline: 23-10-2006 (NEW!))

  6. Maximum Entropy models
    Combining Learners
    Berger et al: A Maximum Entropy Approach to NLP
    Dieterich: Ensemble Methods in Machine Learning
    Alpaydin: Techniques for Combining Multiple Learners
    (
    slides)

  7. Genetic Algorithms, Mitchell, Ch. 9
    (slides)

About WEKA

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

Other Literature and links