Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: Machine learning (17.ESI123)

Type of studies	Title
Master Academic Studies	Power Software Engineering (Year: 1, Semester: Winter)
Master Academic Studies	Mathematics in Engineering (Year: 2, Semester: Winter)

General information:

Category	Theoretical-methodological
Scientific or art field	Applied Computer Science and Informatics
ECTS	6

Acquisition of knowledge of contemporary theory of machine learning, its mathematical basics and related technologies. Students should apply acquired knowledge in anaysis, study and solving real problems.

The student is able to develop new techniques and methods of machine learning and to implement the existing methods in different fields in a new and creative way. The student is trained to analyze, study and solve real problems using the acquired knowledge.

Mathematical basics of machine learning. Selected methods and techniques of machine learning. Selected problems whose solution requires the implementation of methods and techniques of machine learning. Statistical deriving conclusion. Statistical tests. Sampling correlation and regression. Modeling based on computer intelligence (artificial neural networks, decision trees, associative rules, fuzzy logic, support vector machine, genetic algorithm, etc.). Expert systems. Examples of solved and unsolved problems.

Lectures. Computer practice. Consultations. The student is obliged to independently do the project and write a seminar paper.

Authors	Title	Year	Publisher	Language
Bishop, C.M.	Pattern Recognition and Machine Learning	2006	Springer, New York	English
S. Shalev-Schwartz, S. BenDavid	Understanding Machine Learning: From Theory to Algorithms	2014	Cambridge university press	English
M. Magdon-Ismail, Y. AbuMostafa	Learning from Data	2012	AMLBook	English
Ethem Alpaydin	Introduction to Machine Learning	2004	MIT Press	English
Goodfellow, I., Bengio, Y., Courville, A.	Deep Learning	2017	MIT Press, Cambridge	English