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Artificial Intelligence Systems – Laboratory for Machine Intelligence

Artificial Intelligence Systems

Study Program: Electrical Engineering, 2nd Bologna Cycle
Semester: winter semester
Credits: 6

Lecturer: Assoc. Prof. Simon Dobrišek, PhD
Assistant: Asst. Marija Ivanovska, MSc

Course aims

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.

Content

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.

Literature

  • 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.

Additional literature

Prerequisites

The basics of linear algebra, multivariate analysis, optimization, statistics, probability theory, and computer programming.