13M114IS - Intelligent Services and Systems

Course specification
Course title		Intelligent Services and Systems
Acronym		13M114IS
Study programme		Electrical Engineering and Computing
Module		Applied Mathematics, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering
Type of study		master academic studies
Lecturer (for classes)		professor PhD Boško Nikolić professor PhD Dražen Drašković
Lecturer/Associate (for practice)		asst. professor PhD Vladimir Jocović asst. MA Adrian Milaković, electrical and computer engineer
Lecturer/Associate (for OTC)		asst. professor PhD Vladimir Jocović asst. MA Adrian Milaković, electrical and computer engineer
ESPB		6.0	Status	elective
Condition		none
The goal		Introducing students to the basic concepts and techniques of artificial intelligence, machine learning, and intelligent systems. During the course, students will study the most popular models for designing, implementing, and testing these types of applications.
The outcome		In this course, students will learn the foundational principles that drive AI applications and practice implementing some of these systems. The main outcome of the course is to equip students with techniques and tools to tackle new AI and machine learning problems that they may encounter in life and to apply the most appropriate and effective method for solving them based on their knowledge.
Contents
Contents of lectures		Search strategies: algorithms, performance, efficiency, complexity. Game theory algorithms and their application. Production and analytical systems. Planning - problem, and types. Knowledge and reasoning in an uncertain environment. Bayesian networks. Problem-solving strategies. Machine learning: regression, classification, and clustering.
Contents of exercises		Visual simulations of theoretically treated problems. Analyzing and solving practical tasks and demonstrating how to overcome certain problems with artificial intelligence and machine learning techniques.
Literature
Stuart Russell, Peter Norvig, "Artificial Intelligence: A Modern Approach", Pearson, 4th edition (May 2021) (Original title) P. Janičić, M. Nikolić, "Artificial Intelligence" (in Serbian), School of Mathematics, 3rd edition, Belgrade, 2023. Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, "Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python", 1st Edition, Packt Publishing, February 2022. (Original title) Chip Huyen, "Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications", 1st Edition, O'Reilly Media, June 2022. (Original title) Laurence Moroney, "AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence", 1st Edition, O'Reilly Media, November 2020. (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, auditory exercises, independent preparation of several homework assignments, and laboratory exercises with visual simulations.
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		20
Colloquia		50
Seminars		0