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KTH / CSC / Kurser / DD2431 / mi07

Machine Learning DD2431

Welcome to this course in Machine Learning, DD2431, Oct-Dec 2007.

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 go give basic knowledge about the theories and algorithms commonly used in the area.

News

If you have remaining labs to be examined, contact Jenny and/or Simon.

In "Mina Sidor", your result on the exam will only show up as a "P" (Pass) or "F" (Fail). This is due to an error in the system that we are probably not authorized to correct. The correct grade will show up as the final grade for the course once all your labs are finished and reported.

The results from the written examination on Dec 20 are ready. You can see your result via the res command. The result Fx means that you made errors on the A section but may still pass the exam after a small extra examination ("komplettering"). Contact Örjan for a suitable time.

Solution for the exam is available 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
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.

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
Instance based Learning Printable Viewable
Reinforcement Learning Printable Viewable
Genetic Algorithms Printable Viewable
Learning Theory Printable Viewable
Rule based Learning Printable Viewable
Summary Printable Viewable

Lab Instructions

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:

Copyright © Sidansvarig: Örjan Ekeberg <orjan@nada.kth.se>
Uppdaterad 2008-01-28