|01-01-23||Invitation 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.|
Examiner: Prof. Ramin Yasdi.
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.
|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
|Handbook||Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997|