13S113IS - Intelligent Systems

Course specification
Course title Intelligent Systems
Acronym 13S113IS
Study programme Software Engineering
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
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.
URL to the subject page
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.
  1. Stuart Russell, Peter Norvig, "Artificial Intelligence: A Modern Approach", Pearson, 4th edition (May 2021) (Original title)
  2. P. Janičić, M. Nikolić, "Artificial Intelligence" (in Serbian), School of Mathematics, 3rd edition, Belgrade, 2023.
  3. 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)
  4. Chip Huyen, "Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications", 1st Edition, O'Reilly Media, June 2022. (Original title)
  5. 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