13S053NM - Neural Networks

Course specification
Course title		Neural Networks
Acronym		13S053NM
Study programme		Software Engineering
Module
Type of study		bachelor academic studies
Lecturer (for classes)		professor PhD Goran Kvaščev professor PhD Aleksandar Rakić
Lecturer/Associate (for practice)		asst. MA Marija Novičić, electrical and computer engineer professor PhD Sanja Vujnović asst. Aleksa Stojić, electrical and computer engineer professor PhD Aleksandar Rakić
Lecturer/Associate (for OTC)		professor PhD Aleksandar Rakić asst. MA Marija Novičić, electrical and computer engineer
ESPB		6.0	Status	elective
Condition		none
The goal		Introduce students to the basic concepts of neural networks technology, different architectures, internal and external signal representation, learning ability and distributed information processing capability. Design neural network systems for typical engineering applications, including algorithms for signal processing, classification, regression, knowledge extraction.
The outcome		Students will be able to independently analyze and synthesize different types of neural networks that are applied in many areas of engineering and learn to apply various algorithms for learning and training of neural networks and their implementation using MATLAB and Neural Network toolbox
Contents
Contents of lectures		Neural network history and types of problems: function approximation, classification, time series, and dynamic systems modelling. Backpropagation algorithm, generalization, overfitting and initialization. Modeling of time series and dynamic systems using nonlinear NN, classification and clustering, NN classifiers. Deep learning. Convolutional Neural Network (CNN). Long Short-Term Memory (LSTM).
Contents of exercises		Computer-based exercises with demonstrations of algorithms for training and designing neural networks. Solving practical problems from various fields of engineering using neural networks with the help of the Python programming language.
Literature
Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Chin-Teng Lin, C. S. George Lee, Prentice Hall, 1996 (Original title) Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, The MIT Press, 2017 (Original title) Grokking Deep Learning, Andrew Trask, Manning, 2019 (Original title) M. Nielson, Neural Networks and Deep Learning, Determination press, 2015 (Original title) Deep Learning with Python, 2nd Edition, Francois Chollet, Manning, 2021 (Original title)
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
2	2	1
Methods of teaching		Lectures, exercises on computers
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures		0	Test paper	30
Practical lessons		0	Oral examination	0
Projects		30
Colloquia		40
Seminars		0