Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: Selected topics in machine learning (17.DE311)

Native organizations units: Department of Power, Electronic and Telecommunication Engineering

Type of studies	Title
Doctoral Academic Studies	Power, Electronic and Telecommunication Engineering (Godina: 1, Summer)
Doctoral Academic Studies	Biomedical Engineering (Godina: 1, Summer)

General information:

Category	Scientific-professional
Scientific or art field	Telecommunications and Signal Processing
Interdisciplinary	No
ECTS	10

Educational goal:

Introduction to the advanced state-of-the-art machine learning and statistical pattern recognition algorithms.

Educational outcome:

Student will gain knowledge on the advanced techniques and algorithms used in artificial intelligence. Understanding of the techniques on theoretical level, implementation experience including advice on parameter selection, parameter influence and performance monitoring. Ability to implement advanced machine learning algorithms using big data sets.

Course content:

Advanced topic in the field following the trends set by the leading conferences and machine learning journals. Acquired knowledge broadening according to the latest achievements and results in - unsupervised and semi-supervised learning - neural networks and deep learning - probabilistic graphical models - reinforcement learning. Addressing specific application domains and specific data scales (small and big data).

Teaching methods:

Lectures, consultations, development of the project. Study research. Part of the teaching activity on the subject is a self-study research in the field of PhD thesis. Study research includes active monitoring of the scientific sources, organization and execution of experiments and statistical data processing, numerical simulation, writing a paper with a topic close to the scientific and teaching area of the subject of student`s doctoral dissertation.

Literature:

Authors	Title	Year	Publisher	Language
Bishop, C.M.	Pattern Recognition and Machine Learning	2006	Springer, New York	English
Kevin Murphy	Machine Learning: A Probabilistic Perspective	2012	MIT Press	English
Goodfellow, I., Bengio, Y., Courville, A.	Deep Learning	2017	MIT Press, Cambridge	English