An advance introduction to AI emphasizing its theoretical underpinnings. Topics include search, logic, knowledge representation, reasoning planning, decision making under uncertainty, and machine learning.
- Agents, Rationality, Knowledge, Reasoning
- Python Coding Review (Also see C++ Review Notes)
- Problem-solving via Search
- Uninformed Search
- Informed Search, Heuristic Functions
- Local search: Gradient descent (Hill climbing), Simulated Annealing, nondeterminism
- Games, Alpha-Beta Pruning, Intro to Stochastic Games
- Constraint Satisfaction Problems (CSPs)
- Logical agents
- Propositional logic
- Resolution-refutation
- First-order logic (FOL) intro
- Unification; FOL inference and resolution
- Classical Planning
- Resource Scheduling; Overview of Knowledge Representation
- Review of probability, Bayes Rule
- Probabilistic Reasoning: Bayesian Inference
- Bayesian Network examples
- Markov, Hidden Markov Models (HMMs)
- Utility, Markov Decision Processes (MDPs)
- MDPs, Partially-observable MDPs (POMDPs)
- Game theory intro, Intro to learning
- Supervised Learning: Decision Trees
- Intro to Neural Nets, Support Vector Machines (SVMs)
- Reinforcement Learning Introduction