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CPSC 322: Introduction to Artificial Intelligence

CPSC 322: Introduction to Artificial Intelligence (2013, University of British Columbia). Instructor: Professor Alan Mackworth. This course provides an introduction to the field of artificial intelligence. The major topics covered will include reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.

Lecture 23 - Logic: Bottom-up and Top-down Proof Procedures

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Lecture 01 - What is Artificial Intelligence?

Lecture 02 - Representational Dimensions

Lecture 03 - Applications of Artificial Intelligence

Lecture 04 - Representation & Search Framework

Lecture 05 - Breadth-first Search (BFS) and Depth-first Search (DFS)

Lecture 06 - Search with Costs & Heuristic Search

Lecture 07 - Heuristic Search: A*

Lecture 08 - A* Optimality, Cycle Checking

Lecture 09 - Iterative Deepening (IDS) and IDA*

Lecture 10 - Multiple Path Pruning, IDS and IDA*

Lecture 11 - Branch & Bound and Constraint Satisfaction Problems (CSPs)

Lecture 12 - Solving Constraint Satisfaction Problems using Search

Lecture 13 - Arc Consistency in Constraint Satisfaction Problems

Lecture 14 - Generalized Arc-Consistency (GAC) Algorithm and Domain Splitting for CSPs

Lecture 15 - Local Search for Constraint Satisfaction Problems

Lecture 16 - Stochastic Local Search

Lecture 17 - Stochastic Local Search Algorithms

Lecture 18 - Planning: Representation

Lecture 19 - Forward Planning and CSP Planning

Lecture 20 - CSP Planning Wrap Up

Lecture 21 - Intro & Propositional Definite Clause Logic

Lecture 22 - Logic: Proof Procedures, Soundness and Completeness

Lecture 23 - Logic: Bottom-up and Top-down Proof Procedures

Lecture 24 - Logic: Top-Down Procedure, Datalog and Big Picture

Lecture 25 - Probability Theory: Intro

Lecture 26 - Conditional Probability, Bayes Rule, Chain Rule

Lecture 27 - Independence

Lecture 28 - Bayesian Networks Intro

Lecture 29 - Independence and Inference

Lecture 30 - Variable Elimination for Bayes Nets

Lecture 31 - Uncertainty Wrap-up, Decision Theory: Single Decisions

Lecture 32 - Decision Theory: Single and Sequential Decisions

Lecture 33 - Decision Theory: Optimal Policies for Sequential Decisions

Lecture 34 - Perspectives and Final Review