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Course Catalog 01/02


Computer Science, Electrical Engineering

SMD129 Machine Learning 6.0 ECTS credits

TIMEPERIOD:
Quarter II

LANGUAGE:English

EXAMINER
Ramin Yasdi Univ lekt


PREREQUISITES
A-level (first year)

COURSE AIM
To present foundations of methods and techniques in the area of machine learning.

CONTENTS
The field of machine learning is concerned with the question of how to construct computer programs that improve their performance with gaining more
experience in the domain. Machine learning based on concepts and results of many other areas like Artificial Intelligence, Electrical Engineering,
Philosophy, Information Theory, Biology, Cognitive Science, Complexity Theory etc. The thematic of machine learning is not subject to scientist's
skepticism any more as 10 years before. There are numerous industrial applications, that are presented and discussed in several journals and conference
proceedings. At the same time, the theory and algorithms of this research field has been substantially investigated and developed recently.


The goal of this course is to present the foundations of algorithms and theories that forms the kernel of the machine learning. This is usually presumed in
the articles, which makes reading in machine learning difficulties and in-comprehensive. This course aims to assist the students to introduce into this
domain. It presumes no prior knowledge of this area.

Table of contents

Introduction,
Rote Learning,
Learning by Discovery,
Inductive Learning,
Decision Trees,
Logic Oriented Inductive Learning,
Explanation Based Learning,
Data Mining.


TEACHING
Lecture and project.

EXAMINATION
On project.

COURSE GRADE SCALE: 6, 5, 4, 3, VG, G, U

ITEMS/CREDITS

Project work 6.00ECTS

COURSE LITERATURE
Machine Learning by Tom M. Mitchell, The McGraw-Hill Companies, Inc.

Further information: International Office

Course information from the department: http://www.sm.luth.se/~yasdi
Valid for the academic year 01/02.

Web Editor: Karin.Lindholm@dc.luth.se

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LULEÅ UNIVERSITY OF TECHNOLOGY
University Campus, Porsön, 971 87 Luleå. Tel. +46 (0) 920-91 000, fax +46 (0) 920-91 399
Last edited 2001-12-17