Lecture 1 | Intro 1: What is Artificial Intelligence?

Lecture 2 | Intro 2: Representational Dimensions

Lecture 3 | Intro 3: Applications of AI

Lecture 4 | Search 1: Representation & Search Framework

Lecture 5 | Search 2: BFS and DFS

Lecture 6 - Search 3: Search with Costs & Heuristic Search

Lecture 7 - Search 4: Heuristic Search: A*

Lecture 8 | Search 5: A* Optimality, Cycle Checking

Lecture 9 | Search 6: Iterative Deepening (IDS) and IDA*

Lecture 10 | Search 7: Multiple Path Pruning, IDS and IDA*

Lecture 12 | CSP 2: Solving CSP Using Search

Lecture 13 | CSP 3: Arc Consistency

Lecture 14 | CSP 4: GAC Algorithm and Domain Splitting for CSPs

Lecture 15 | CSP 5: Local Search

Lecture 16 | CSP 6: Stochastic Local Search

Lecture 17 | CSP 7: Stochastic Local Search Algorithms

Lecture 18 | Planning 1: Representation

Lecture 19 | Planning 2: Forward Planning and CSP Planning

Lecture 20 | Planning 3: CSP Planning Wrap Up

Lecture 21 | Logic 1: Intro & Propositional Definite Clause Logic

Lecture 22 - Logic 2: Proof Procedures, Soundness and Completeness

Lecture 23 | Logic 3: Bottom-up and Top-down Proof Procedures

Lecture 25 | Uncertainty 1: Probability Theory: Intro

Lecture 31 | Decision Theory 1: Uncertainty Wrap-Up, Single Decisions

Lecture 26 | Uncertainty 2: Conditional Probability, Bayes Rule, Chain Rule

Lecture 27 - Uncertainty 3: Independence

Lecture 28 | Uncertainty 4: Bayesian Networks Intro

Lecture 29 | Uncertainty 5: Independence and Inference

Lecture 30 | Uncertainty 6: Variable Elimination for Bayes Nets

Lecture 32 | Decision Theory 2: Single and Sequential Decisions

Lecture 33 | Decision Theory 3: Optimal Policies for Sequential Decisions

Lecture 34 | Perspectives and Final Review

Lecture 11 | CSP 1: Branch & Bound, CSP: Intro

Lecture 24 | Logic 4: Top-Down Procedure, Datalog and Big Picture