Introduction to AI
- Flavours of AI: strong and weak, neat and scruffy, symbolic and sub-symbolic, knowledge-based and data-driven.
- The computational metaphor. What is computation? Church-Turing thesis. The Turing test. Searle's Chinese room argument.
Search
- Finding satisfactory paths and outcomes; chosen from: depth-first and breadth-first, iterative deepening, evolutionary algorithms, hill-climbing and gradient descent, beam search and best-first. Finding optimal paths: branch and bound, dynamic programming, A*.
Representing Knowledge
- Production rules, monotonic and non-monotonic logics, semantic nets, frames and scripts, description logics.
Reasoning and Control
- Data-driven and goal-driven reasoning, AND/OR graphs, truth-maintenance systems, abduction and uncertainty.
Reasoning under Uncertainty
- Probabilities, conditional independence, causality, Bayesian networks, noisy-OR, d-separation, belief propagation.
Machine Learning
- Inductive and deductive learning, unsupervised and supervised learning, reinforcement learning, concept learning from examples, Quinlan's ID3, classification and regression trees, Bayesian methods.
Key Application Areas, selected from:
- Expert system, decision support systems
- Speech and vision
- Natural language processing
- Information Retrieval
- Semantic Web