Machine Learning DD2431

Welcome to this course in Machine Learning, DD2431, Oct-Dec 2008.

How can a computer program automatically improve its behavior based on previous experience? Such questions often come up when designing game-playing programs, but also in robotics or when designing adaptive man-machine interfaces. The subject touches on artificial intelligence, statistics, information theory, biology and control theory. The goal of this course is to give basic knowledge about the theories and algorithms commonly used in the area.

News

Re-exam. The reexam Monday 2009-06-08 is now corrected. You can see your results via the res-system and/or by visiting the CSC student office (Osquars Backe 2).
The ordinary exam is corrected. You can see your results via the res-system and/or by visiting the CSC student office (Osquars Backe 2). You can find the exam here and the solutions here.

Faculty

Course leader: Örjan Ekeberg
Lecturers: Örjan Ekeberg and Hedvig Kjellström

Literature

Textbook:: Mitchell: Machine Learning
McGraw-Hill, 1997
ISBN: 0-07-115467-1
Available at the student union bookstore (price 675 SEK).
Article on Adaboost:: R.E. Schapire. A brief introduction to boosting. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.
Article on Bagging:: J.R. Quinlan. Bagging, boosting, and C4.5 In Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference, pages 725-730, Menlo Park, August 4-8 1996, AAAI Press / MIT Press

Teaching

Teaching is in the form of lectures plus instructions in connection with the lab assignments. All lectures are in English.

Schedule and questions of interest for the lectures.

Slides from the lectures

Introduction	Printable	Viewable
Concept Learning	Printable	Viewable
Decision Trees	Printable	Viewable
Artificial Neural Networks	Printable	Viewable
Bayesian Learning	Printable	Viewable
Bagging and Boosting	Printable	Viewable
Reinforcement Learning	Printable	Viewable
Genetic Algorithms	Printable	Viewable
Learning Theory	Printable	Viewable
Support Vector Machines	Printable	Viewable
Rule based Learning	Printable	Viewable
Summary	Printable	Viewable

Lab Instructions

Booking of lab examination times is done through our web-based booking system.

Note that the lab examinations now take place in the sofas outside lab-room Gul. Be aware that you only have 10 minutes at your disposal so come well prepared!

Also note that we have sheduled these slots together with the ANN-course. You must therefore make a note that you wish to be examined on a ML-lab.

Instructions for Lab 1 (Concept Learning) [bonus deadline 2008-11-14]
Instructions for Lab 2 (Bayes Classifiers and Boosing) [bonus deadline 2008-12-05]
Debugging help for Lab 2
Instructions for Lab 3 (Reinforcement Learning) [bonus deadline 2008-11-21]
Instructions for Lab 4 (Support Vector Machines, updated 2008-11-26) [bonus deadline 2008-12-12]

Note the reversed order of the deadlines for labs 2 and 3.

Files for the labs

If you want to work with the labs on your own computer you will need the files from /info/mi08/labs. They can be downloaded here in the form of a gzipped tar-archive.

Course Examination

Written Exam (Tentamen)
Four Lab Assignments

You do not have to register separately for the written exam.

Last years exam is available here: