Artificial Intelligence: A Modern Approach
by Russel and Norvig. The book's homepage includes slides, a code repository, data and errata.
Lecture slides, exercises and solutions
- Introduction to AI
- Intelligent agents: agent types, environment types
- Solving problems by searching: states and actions,
toy examples and real world problems, search
strategies, comparison of search strategies, constraint satisfaction problems, backtracking search, constraint propagation, heuristics for CSPs, local search
- Informed search methods: Greedy search, A* search,
Iterative deepening search, SMA*
- Game playing: minimax
algorithm, alpha-beta pruning, games with stochastic elements, state of the art
- uncertainty: axioms of probability, Bayes rule, application of Bayes
- Probabilistic reasoning systems: belief networks, conditional
independence in belief networks, inference in belief networks, inference in
multiply connected belief networks, sampling methods.
- Making simple decisions: basic utility theory, decision networks, value of perfect information
- making complex decisions: sequential decision
problems, value iteration, policy iteration.
- Learning from observations: Inductive learning, Learning decision trees, computational learning
AI Resources & Links
Education Repository This repository is a central registry of
resources related to Artificial Intelligence (AI) education.
Intelligence links organized by Yahoo.
- AI on the web
A very comprehensive and well-organized directory of AI
resources (by Stuart Russel.
- AI resources A
comprehensive list of resources composed by
the The Institute for Information Technology of Canada.
- IAAI Israeli Association of