Course Number: LIX004M5
Instructor: Jörg Tiedemann, Alfa-informatica,
Off. Hours. 15:15-16 pm Mon. (after lecture)
Announcements Autumn 2005
Sept 5 - Oct. 21
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
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
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:
The exam is open book. Books, notes and calculators may be
used. There is a sample exam, taken
from the Weka site.
Preliminary program: (tentative)
- Organization, Inspiration, Mitchell Ch.1 &
Intro Machine Learning, Mitchell Ch.2
- Decision Trees, Mitchell Ch.3
Lab 1 - Decision Trees
- Bayesian Learning I, Mitchell, Ch.6
- Bayesian Learning II, (H)MMs and EM, Mitchell, Ch.6 and
Manning & Schütze,Ch. 9
Lab 2 - Naive Bayes
(deadline: 21-10-2005 !!!)
- Instance-Based Learning, Mitchell, Ch.8
- Support Vector Machines, Francisco Borges
Lab 3 -
continue working on assignment 2
- Log-linear models, MaxEnt and further topics
Lab 4 - compare classifiers
Other Literature and links