1,1,0

Lecture type	Total
Lectures	45

* Load is given in academic hour (1 academic hour = 45 minutes)

Machine Learning is a branch of artificial intelligence concerned with the design of algorithms that improve their performance based on empirical data. It has become one of the most active and exciting areas of computer science research, in large part because of its wide-spread applicability, ranging from data mining and pattern recognition to robotics, computational biology, and computational linguistics. This course gives an in-depth coverage of the theory and principles of machine learning, and gives an overview of machine learning applications. The course covers three main approaches to machine learning: supervised learning (classification and regression), unsupervised (clustering), and reinforcement learning. The course teaches how to design and implement a machine learning system and how to evaluate its performance.

1. Introduction to Machine Learning; Ethem Alpaydin; The MIT Press; 2009; ISBN: 978-0262012430
2. Pattern Recognition and Machine Learning; Christopher M. Bishop; Springer; 2007; ISBN: 978-0387310732
3. Machine Learning: A Probabilistic Perspective; Kevin P. Murphy; The MIT Press; 2012; ISBN: 978-0262018029
4. Machine Learning: An Algorithmic Perspective; Stephen Marsland; Chapman and Hall/CRC; 2009; ISBN: 978-1420067187
5. The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Trevor Hastie, Robert Tibshirani, Jerome Friedman; Springer; 2009; ISBN: 978-0387848570
6. Info Theory, Inference, and Learning Algorithms;MacKay;2003;Cambridge University Press

Teorijski predmeti profila - profile Computer Science

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