Syllabus
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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
Annotation
An introductory course covering basic concepts and methods of artificial intelligence. The course assumes knowledge of logic and probability theory at the undergraduate level.