Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: Neural networks (17.E2512)

Native organizations units: Sub-department for Applied Computer Science and Informatics

Type of studies	Title
Master Academic Studies	Artificial Intelligence and Machine Learning (Year: 1, Semester: Winter)
Master Academic Studies	Information and Analytics Engineering (Year: 1, Semester: Winter)
Master Academic Studies	Software Engineering and Information Technologies (Year: 1, Semester: Winter)
Master Academic Studies	Computing and Control Engineering (Year: 1, Semester: Winter)
Master Academic Studies	Information Engineering (Year: 1, Semester: Winter)

General information:

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

Students become familiar with concepts, techniques and application examples of neural networks.

Understanding basic principles and techniques behind neural networks and the ability to apply them in solving different types of problems.

Introduction into neural networks: perception, a mathematical model of a neuron, backpropagation algorithm. Deep neural network architectures: convolution networks, recurrent networks, generative models, etc. Visualizing neural networks. Neural network training: algorithms and techniques.

Teaching methods include: lectures, laboratory classes, homework assignments, and consultations. Lectures involve presenting the course materials using the necessary didactic tools while encouraging the students to participate actively. Laboratory classes (exercises) are realized through assignments that can be done independently or with the help of teaching assistants, as well as through homework assignments.

Authors	Title	Year	Publisher	Language
Goodfellow, I., Bengio, Y., Courville, A.	Deep Learning	2017	MIT Press, Cambridge	English
Francois Chollet	Deep Learning with Python	2017	Manning Publications	English
Wu, G., Shen, D., Sabuncu, M.R.	Machine Learning and Medical Imaging	2016	Elsevier	English