Course Number: LIX004M5
Instructor: Jörg Tiedemann, Alfa-informatica,
Announcements Autumn 2006
Sept 4 - Oct. 20
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
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.
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
Guidelines for writing lab reports
- All reports must be your own work. No copying.
- You may write your reports in English or Dutch. Be assured that you will
not lose marks simply for making mistakes in your English (or Dutch - but I
can't judge that anyway ;-))
- Please submit reports by email to me (firstname.lastname@example.org) in either
postscript (.ps), plain text (.txt) or pdf format
or on paper in my mail
box in the Harmoniegebouw, 4th floor
- Each report is marked out of 10. You have one week to hand in the report
except for the last one (2 weeks).
If you are up to 1 week late, the report will get half marks. More than 1
week late and the report gets no marks.
- Your report should include the questions for the relevant assignment (but
not the introduction).
- Give concise but complete answers to the questions.
- Organization, Inspiration &
Intro Machine Learning, Mitchell Ch.1, Ch.5
- Inductive learning as search with bias, Mitchell Ch.2
Decision Trees, Mitchell Ch.3
Lab 1 - Decision Trees
- Instance-Based Learning, Mitchell, Ch.8
Lab 2 - Instance-Based Learning
- Bayesian learning, Mitchell Ch.6
Lab 3 - Learner comparison/combination
- Sequential data & Markov Models
Manning & Schütze,Ch. 9,
Bilmes: What HMMs can do
Lab 4 - Markov Models
(deadline: 23-10-2006 (NEW!))
- Maximum Entropy models
Berger et al: A Maximum Entropy Approach to NLP
Dieterich: Ensemble Methods in Machine Learning
Alpaydin: Techniques for Combining Multiple Learners
- Genetic Algorithms, Mitchell, Ch. 9
Some information about WEKA and how to use it (old):
Other Literature and links