Machine learning introduces the student to a broad area of artificial intelligence that aims at developing computer systems that automatically improve their performance with experience. Machine learning algorithms are widely employed and are encountered daily. Examples are automatic recommendations when buying a product or voice recognition software that adapts to your voice. The course will present both the basic, and the state-of-the-art techniques of machine learning. The practical use of the presented techniques and the problems of developing real machine-learning applications will be emphasized. The course is accompanied by practical labs that help the student understand the working of machine learning algorithms. After completing this course students will be able to use machine learning algorithms when encountered with a learning problem. Additionally students will be able to judge the quality of the model that is learned.
Students entering the course should have a working knowledge of probability theory/statistics, logic and algorithms (including programming).
I.H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, June 2005, ISBN 0-12-088407-0 T. Mitchell (1997). Machine Learning, McGraw-Hill, ISBN 0-07-042807-7.