13S113IS - Intelligent Systems

Course specification
Course title		Intelligent Systems
Acronym		13S113IS
Study programme		Software Engineering
Module
Type of study		bachelor academic studies
Lecturer (for classes)		professor PhD Boško Nikolić professor PhD Dražen Drašković
Lecturer/Associate (for practice)		asst. MA Vladimir Jocović, electrical and computer engineer asst. MA Adrian Milaković, electrical and computer engineer
Lecturer/Associate (for OTC)		asst. MA Vladimir Jocović, electrical and computer engineer 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
URL to the subject page		http://ri4es.etf.bg.ac.rs/
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