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CPSC 322: Introduction to Artificial Intelligence (2013, University of British Columbia). Instructor: Professor Alan Mackworth.
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English
English [CC]
FREE
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Description
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.
Course content
- Lecture 01 – What is Artificial Intelligence? Unlimited
- Lecture 02 – Representational Dimensions Unlimited
- Lecture 03 – Applications of Artificial Intelligence Unlimited
- Lecture 04 – Representation & Search Framework Unlimited
- Lecture 05 – Breadth-first Search (BFS) and Depth-first Search (DFS) Unlimited
- Lecture 06 – Search with Costs & Heuristic Search Unlimited
- Lecture 07 – Heuristic Search: A* Unlimited
- Lecture 08 – A* Optimality, Cycle Checking Unlimited
- Lecture 09 – Iterative Deepening (IDS) and IDA* Unlimited
- Lecture 10 – Multiple Path Pruning, IDS and IDA* Unlimited
- Lecture 11 – Branch & Bound and Constraint Satisfaction Problems (CSPs) Unlimited
- Lecture 12 – Solving Constraint Satisfaction Problems using Search Unlimited
- Lecture 13 – Arc Consistency in Constraint Satisfaction Problems Unlimited
- Lecture 14 – Generalized Arc-Consistency (GAC) Algorithm and Domain Splitting for CSPs Unlimited
- Lecture 15 – Local Search for Constraint Satisfaction Problems Unlimited
- Lecture 16 – Stochastic Local Search Unlimited
- Lecture 17 – Stochastic Local Search Algorithms Unlimited
- Lecture 18 – Planning: Representation Unlimited
- Lecture 19 – Forward Planning and CSP Planning Unlimited
- Lecture 20 – CSP Planning Wrap Up Unlimited
- Lecture 21 – Intro & Propositional Definite Clause Logic Unlimited
- Lecture 22 – Logic: Proof Procedures, Soundness and Completeness Unlimited
- Lecture 23 – Logic: Bottom-up and Top-down Proof Procedures Unlimited
- Lecture 24 – Logic: Top-Down Procedure, Datalog and Big Picture Unlimited
- Lecture 25 – Probability Theory: Intro Unlimited
- Lecture 26 – Conditional Probability, Bayes Rule, Chain Rule Unlimited
- Lecture 27 – Independence Unlimited
- Lecture 28 – Bayesian Networks Intro Unlimited
- Lecture 29 – Independence and Inference Unlimited
- Lecture 30 – Variable Elimination for Bayes Nets Unlimited
- Lecture 31 – Uncertainty Wrap-up, Decision Theory: Single Decisions Unlimited
- Lecture 33 – Decision Theory: Optimal Policies for Sequential Decisions Unlimited
- Lecture 34 – Perspectives and Final Review Unlimited
Instructor
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