Introduction to Artificial Intelligence (Alan Mackworth / U of British Columbia)

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source: Alan Mackworth     2013年11月21日
Official course page at: http://www.cs.ubc.ca/~mack/CS322/
Undergraduate level computer science course by Dr. Alan Mackworth, given at the University of British Columbia.

Lecture 1 | Intro 1: What is Artificial Intelligence? 49:43
Lecture 2 | Intro 2: Representational Dimensions 48:42
Lecture 3 | Intro 3: Applications of AI 41:43
Lecture 4 | Search 1: Representation & Search Framework 47:38
Lecture 5 | Search 2: BFS and DFS 47:54
Lecture 6 - Search 3: Search with Costs & Heuristic Search 46:38
Lecture 7 - Search 4: Heuristic Search: A* 46:19
Lecture 8 | Search 5: A* Optimality, Cycle Checking 43:41
Lecture 9 | Search 6: Iterative Deepening (IDS) and IDA* 43:48
Lecture 10 | Search 7: Multiple Path Pruning, IDS and IDA* 47:33
Lecture 11 | CSP 1: Branch & Bound, CSP: Intro 49:25
Lecture 12 | CSP 2: Solving CSP Using Search 49:44
Lecture 13 | CSP 3: Arc Consistency 48:54
Lecture 14 | CSP 4: GAC Algorithm and Domain Splitting for CSPs 48:23
Lecture 15 | CSP 5: Local Search 49:51
Lecture 16 | CSP 6: Stochastic Local Search 47:16
Lecture 17 | CSP 7: Stochastic Local Search Algorithms 47:26
Lecture 18 | Planning 1: Representation 48:23
Lecture 19 | Planning 2: Forward Planning and CSP Planning 47:56
Lecture 20 | Planning 3: CSP Planning Wrap Up 46:44
Lecture 21 | Logic 1: Intro & Propositional Definite Clause Logic 48:51
Lecture 22 - Logic 2: Proof Procedures, Soundness and Completeness 51:20
Lecture 23 | Logic 3: Bottom-up and Top-down Proof Procedures 49:18
Lecture 24 | Logic 4: Top-Down Procedure, Datalog and Big Picture 47:54
Lecture 25 | Uncertainty 1: Probability Theory: Intro 49:32
Lecture 26 | Uncertainty 2: Conditional Probability, Bayes Rule, Chain Rule 49:23
Lecture 27 - Uncertainty 3: Independence 46:12
Lecture 28 | Uncertainty 4: Bayesian Networks Intro 47:09
Lecture 29 | Uncertainty 5: Independence and Inference 49:49
Lecture 30 | Uncertainty 6: Variable Elimination for Bayes Nets 48:57
Lecture 31 | Decision Theory 1: Uncertainty Wrap-Up, Single Decisions 49:19
Lecture 32 | Decision Theory 2: Single and Sequential Decisions 51:02
Lecture 33 | Decision Theory 3: Optimal Policies for Sequential Decisions 47:16
Lecture 34 | Perspectives and Final Review 52:23