Syllabus
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.