Mashine Learning
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01-01-23Invitation to the cource: Machine learning. The first meeting will be held January 26 at 10.00 in room A 3217. For participation in the course please send an email to Prof. Ramin Yasdi.


Points: 5.

Eligible Students:


Examiner: Prof. Ramin Yasdi.

Course Plan

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.

ContentsSceduled timePresenter
Introduction X hrs Prof. Ramin Yasdi
Rote Learning X hrs Prof. Ramin Yasdi
Learning by Discovery X hrs Prof. Ramin Yasdi
Inductive Learning X hrs Prof. Ramin Yasdi
Decision Trees X hrs Prof. Ramin Yasdi
Logic oriented Inductive Learning X hrs Prof. Ramin Yasdi
Explanation based Learning X hrs Prof. Ramin Yasdi
Bayesian Learning X hrs Prof. Ramin Yasdi
Data mining X hrs Prof. Ramin Yasdi

Total sceduled time: X hrs


Short Name Reference
Handbook Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997

Laboratory work

[Announcements] [Description] [Course plan] [Literature] [Laboratory work]

Last modified 2001-01-24 by olovm
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