Introduction to Artificial Intelligence with Alan Mackworth

Video Lectures

Displaying all 34 video lectures.
I. Introduction to AI
Lecture 1
Intro 1: What is Artificial Intelligence?
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Intro 1: What is Artificial Intelligence?
Lecture 2
Intro 2: Representational Dimensions
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Intro 2: Representational Dimensions
Lecture 3
Intro 3: Applications of AI
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Intro 3: Applications of AI
II. Search
Lecture 4
Search 1: Representation & Search Framework
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Search 1: Representation & Search Framework
Lecture 5
Search 2: BFS and DFS
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Search 2: BFS and DFS
Lecture 6
Search 3: Search with Costs & Heuristic Search
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Search 3: Search with Costs & Heuristic Search
Lecture 7
Search 4: Heuristic Search: A*
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Search 4: Heuristic Search: A*
Lecture 8
Search 5: A* Optimality, Cycle Checking
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Search 5: A* Optimality, Cycle Checking
Lecture 9
Search 6: Iterative Deepening (IDS) and IDA*
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Search 6: Iterative Deepening (IDS) and IDA*
Lecture 10
Search 7: Multiple Path Pruning, IDS and IDA*
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Search 7: Multiple Path Pruning, IDS and IDA*
III. Constraint Satisfaction Problems (CSP)
Lecture 11
CSP 1: Branch & Bound, CSP: Intro
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CSP 1: Branch & Bound, CSP: Intro
Lecture 12
CSP 2: Solving CSP Using Search
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CSP 2: Solving CSP Using Search
Lecture 13
CSP 3: Arc Consistency
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CSP 3: Arc Consistency
Lecture 14
CSP 4: GAC Algorithm and Domain Splitting for CSPs
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CSP 4: GAC Algorithm and Domain Splitting for CSPs
Lecture 15
CSP 5: Local Search
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CSP 5: Local Search
Lecture 16
CSP 6: Stochastic Local Search
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CSP 6: Stochastic Local Search
Lecture 17
CSP 7: Stochastic Local Search Algorithms
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CSP 7: Stochastic Local Search Algorithms
IV. Planning
Lecture 18
Planning 1: Representation
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Planning 1: Representation
Lecture 19
Planning 2: Forward Planning and CSP Planning
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Planning 2: Forward Planning and CSP Planning
Lecture 20
Planning 3: CSP Planning Wrap Up
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Planning 3: CSP Planning Wrap Up
V. Logic
Lecture 21
Logic 1: Intro & Propositional Definite Clause Logic
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Logic 1: Intro & Propositional Definite Clause Logic
Lecture 22
Logic 2: Proof Procedures, Soundness and Completeness
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Logic 2: Proof Procedures, Soundness and Completeness
Lecture 23
Logic 3: Bottom-up and Top-down Proof Procedures
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Logic 3: Bottom-up and Top-down Proof Procedures
Lecture 24
Logic 4: Top-Down Procedure, Datalog and Big Picture
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Logic 4: Top-Down Procedure, Datalog and Big Picture
VI. Uncertainty
Lecture 25
Uncertainty 1: Probability Theory: Intro
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Uncertainty 1: Probability Theory: Intro
Lecture 26
Uncertainty 2: Conditional Probability, Bayes Rule, Chain Rule
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Uncertainty 2: Conditional Probability, Bayes Rule, Chain Rule
Lecture 27
Uncertainty 3: Independence
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Uncertainty 3: Independence
Lecture 28
Uncertainty 4: Bayesian Networks Intro
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Uncertainty 4: Bayesian Networks Intro
Lecture 29
Uncertainty 5: Independence and Inference
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Uncertainty 5: Independence and Inference
Lecture 30
Uncertainty 6: Variable Elimination for Bayes Nets
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Uncertainty 6: Variable Elimination for Bayes Nets
VII. Decision Theory
Lecture 31
Decision Theory 1: Uncertainty Wrap-Up, Single Decisions
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Decision Theory 1: Uncertainty Wrap-Up, Single Decisions
Lecture 32
Decision Theory 2: Single and Sequential Decisions
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Decision Theory 2: Single and Sequential Decisions
Lecture 33
Decision Theory 3: Optimal Policies for Sequential Decisions
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Decision Theory 3: Optimal Policies for Sequential Decisions
VIII. Conclusion
Lecture 34
Perspectives and Final Review
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Perspectives and Final Review