Introduction to AI

Topics covered

• Search & planning: Uninformed and informed search, heuristics, A*, constraint satisfaction, backtracking, minimax, and alpha-beta pruning.
• Sequential decisions: Markov decision processes, reinforcement learning, decision networks, value of information, and POMDPs.
• Probabilistic inference: Bayesian networks, inference, sampling, and Naive Bayes.
• State estimation: Hidden Markov models and particle filtering.