Study program: Electrical Engineering, UN 2nd bologna cycle
Semester: Winter semester
Professor: assoc. prof. Simon Dobrišek, PhD
To provide students with an understanding of the basic mathematical and computational approaches to artificial intelligence, the concepts of artificial intelligent systems, and examples of implementations of such systems.
The course lectures cover the most important topics from the area of artificial intelligence:
- Introduction to artificial intelligent systems: artificial perception, artificial intelligence, soft computing, machine learning, autonomous agents, and ambient intelligence.
- Intelligent problem solving: problem decomposition and reduction, graph representation of problems, and graph search – exhaustive and heuristic search algorithms.
- Case study: assembly automation.
- Expert systems: expert system components and human interfaces, procedural and declarative knowledge, and reasoning process.
- Knowledge representation: production rules, fuzzy production rules, and representation based on the Petri nets.
- Inference: forward and backward chaining, fuzzy inference, and probabilistic inference.
- Case study: knowledge-based computer vision systems.
- Knowledge from experimental data: multivariate regression with artificial neural networks and support vector machines.
- Multi-agent systems: intelligent agent, multi-agent systems, agent communication language.
- Case study: FIPA-compliant multi-agent platforms.
- Russel S., Norvig P.: Artificial Intelligence, A Modern Approach (Third edition), Prentice Hall. 2010.
- Mohri M., Rostamizadeh A., Talwalkar, A. : Foundations of Machine Learning, The MIT Press, 2012.
- Kecman V.: Learning and Soft computing, MIT Press, 2001.
The basics of linear algebra, multivariate analysis, optimization, statistics, probability theory, and computer programming.
More information on the course as well as material needed for the lab assignments is available from the E-classroom.
What will I learn?
After completing this course, the student will be able to demonstrate knowledge and understanding of:
- construction of systems based on the use of the methods of artificial intelligence;
- modelling of specific human mental abilities (general problem solving, learning, and reasoning);
- graph search methods, the integration of human knowledge into artificial intelligent systems, and searching for regularities in data.
By the end of the course, students will have developed the following transferable skills:
- use of information technology: the use of open source development tools (OpenCV, WEKA, CLIPS, JADE), programming environments (Matlab, Netbeans), programming languages (Java, Prolog);
- problem solving: problem analysis, algorithm design, implementation and testing of a program.