Lectures
The main course material, the lecture presentations as PDF documents, are listed below in the lecture schedule. Notice that there are often changes in some of the material after the lecture (small corrections and extensions), and it pays off downloading the material later (again), so that you have an up-to-date version.
Lecture 1: January 5: Introduction: arrangements, symbolic vs. non-symbolic AI, general models of intelligent systems (slides 4-on-1, slides)
Lecture 2: January 12: Search algorithms: breadth-first, depth-first, A* (slides 4-on-1, slides, updated March 17)
Lecture 3: January 19: Heuristics (lower bounds), suboptimal informed search, dynamic programming, general-purpose modeling language NDL (slides 4-on-1, slides, updated February 1)
Lecture 4: January 26: Logic in AI: background, constraint solving, formulas as sets (slides 4-on-1, slides)
Lecture 5: February 2: State-space search with logic and constraints (slides 4-on-1, slides, updated March 1)
Lecture 6: February 9: Symbolic breadth-first search; planning with uncertainty and Markov decision processes; Reinforcement Learning (slides 4-on-1, slides, updated March 24)
Lecture 7: February 23: Uncertainty in decision-making, partial observability (slides 4-on-1, slides, updated March 2)
Lecture 8: March 2: Decision-making in game-theoretic and adversarial settings, multi-agent decision-making and coordination (slides 4-on-1, slides)
Lecture 9: March 9: Multi-agent decision making (preference aggregation), game tree search algorithms (systematic and stochastic) (slides 4-on-1, slides)
Lecture 10: March 16: Applications of AI technologies in autonomous systems, commerce, distributed systems (slides 4-on-1, slides)
Lecture 11: March 23: Past, future, summary (slides 4-on-1, slides)