|Course title:||CS570 - Topics in AI: Machine Learning|
|Course description:||One of the many definitions of Machine Learning (ML) is "Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population" (Simon, 1983). Practically this means developing computer programs that automatically improve their performance through experience. The course covers the basic concepts and techniques of Machine Learning from both theoretical and practical perspective. The material includes classical ML approaches as Version Spaces, Decision Trees and Bayesian Learning as well as new approaches as Relational Learning and Minimum Description Length Principle (MDL). All topics are accompanied with hands-on exercises with implementations of ML algorithms.|
|Course dates:||May 27, 2008 - Jul 17, 2008|
|Machine Learning, Tom Mitchell, McGraw , 1997, 0-07-042807-7|
|SWI-Prolog ( http://www.swi-prolog.org/ ) - free Prolog compiler, licensed under the GNU Public License. Portable to many platforms, including almost all Unix/Linux platforms, Windows, MacOS X and many more. More information about using SWI-Prolog for this course will be provided in the course content modules.|
|Grading will be based on five assignments and class participation through
The letter grade will be determined by the following grading scale:
Late assignments will be marked one letter grade down for each 3 days they are late.
It is expected that all students will conduct themselves in an honest manner and NEVER claim work which is not their own. Violating this policy will result in a substantial grade penalty or a final grade of F.
|General Web Resources|
|Name:||Zdravko Markov, Ph.D|
|Biography:||Dr. Zdravko Markov has an M.S. in Mathematics and Computer Science and a Ph.D. in Artificial Intelligence. He has been teaching and doing research in the area of Machine Learning for more than 15 years. Recently he developed a novel approach to conceptual clustering and is studying its application to Data Mining tasks. Dr. Markov has published 4 textbooks and more than 50 research papers in conference proceedings and journals. His most recent book (co-authored with Daniel Larose) is “Data Mining The Web: Uncovering Patterns in Web Content, Structure, and Usage", published by Wiley in 2007. Dr. Markov’s CCSU courses are in the areas of Computer Architecture and Design, Computing and Communication technology, Machine Learning, Data and Web Mining.|