Machine Learning Basic Information M096 (3+2+0) - 7 ECTS credits Course objectives are to familiarize students with theory and principles of machine learning and applications. Special emphasis will be given to supervised learning methods (classification and regression) and unsupervised learning methods (clustering). You can access the course content at the following link: PDF Teachers Instructor: Dr. Kristian Sabo, Full Professor Instructor: Dr. Domagoj Matijević, Associate Professor Supporting Instructor: Antonio Jovanović Basic literature Christopher Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, Berlin, 2006. S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge Press, 2014. Additional literature Teaching materials The materials are available on the internal Teams channel of the course, through which all internal communication takes place. Students are required to register on the course’s Teams channel. The channel code for joining the course can be found in the schedule.