AI is the study of building systems that exhibit intelligent behaviour. This course covers uninformed and informed search, knowledge representation, constraint satisfaction, planning, reasoning under uncertainty, and a survey of learning-based approaches.
Outcomes
- Apply uninformed and informed search algorithms to planning problems
- Model a problem domain using constraint satisfaction
- Reason under uncertainty with probabilistic representations
- Evaluate AI techniques by their assumptions, complexity, and failure modes
Outline
Start →- 01 Search Algorithms BFS, DFS, A*, and heuristic search: finding solutions in large state spaces.
- 02 Knowledge Representation Propositional and first-order logic, inference rules, and knowledge bases.
- 03 Planning STRIPS, state-space planning, and representing action schemas for automated plan generation.
- 04 Probabilistic Reasoning Bayesian networks, conditional probability, and reasoning under uncertainty.
- 05 Overview of Machine Learning Supervised, unsupervised, and reinforcement learning as approaches to AI from data.