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An introductory course covering basic concepts and methods of artificial intelligence. The course assumes
knowledge of logic and probability theory at the undergraduate level.
Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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In order to pass the course, the student must obtain the credit for the seminar and pass an exam. The credit is given for solving assignments from the seminar. The nature of study verification excludes the possibility of its repetition. The exam is oral with time for written preparation. The requirements correspond to the syllabus in the extent presented during the lectures. A part of the exam may be the design of an algorithm for a given problem. Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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S. Russell, P. Norvig: Artificial Intelligence; A Modern Approach, 2010 V. Mařík, O. Štepánková, J. Lažanský a kol.: Umělá Inteligence, 1-6. Academia, Praha
Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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1. Basic terminology, history, background 2. Problem solving via search (A* and others) 3. Constraint satisfaction 4. Logical reasoning (forward and backward chaining, resolution, SAT) 5. Probabilistic reasoning (Bayesian networks) 6. Knowledge representation (situation calculus, Markovian models) 7. Automated planning 8. Markov decision processes 9. Games and theory of games 10. Machine learning (decision trees, regression, reinforcement learning) 11. Philosophical and ethical aspects Last update: Töpfer Pavel, doc. RNDr., CSc. (30.01.2018)
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