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Artificial Intelligence. Instructor: Prof. Deepak Khemani, Department of Computer Science and Engineering, IIT Madras.
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Description
This course provides an introduction to artificial intelligence. Topics include Introduction: Overview and Historical Perspective, Turing test, Physical Symbol Systems and the scope of Symbolic AI, Agents; State Space Search: Depth First Search, Breadth First Search, DFID; Heuristic Search: Best First Search, Hill Climbing, Beam Search, Tabu Search; Randomized Search: Simulated Annealing, Genetic Algorithms, Ant Colony Optimization; Finding Optimal Paths: Branch and Bound, A*, IDA*, Divide and Conquer approaches, Beam Stack Search; Problem Decomposition: Goal Trees, AO*, Rule Based Systems, Rete Net; Game Playing: Minimax Algorithm, Alpha-Beta Algorithm, SSS*; Planning and Constraint Satisfaction: Domains, Forward and Backward Search, Goal Stack Planning, Plan Space Planning, Graphplan, Constraint Propagation; Logic and Inferences: Propositional Logic, First Order Logic, Soundness and Completeness, Forward and Backward chaining. (from nptel.ac.in)
Course content
- Lecture 01 – Artificial Intelligence: Introduction Unlimited
- Lecture 02 – History of Artificial Intelligence: Mechanical Aspects Unlimited
- Lecture 03 – History of Artificial Intelligence: Philosophical Aspects Unlimited
- Lecture 04 – History of Artificial Intelligence Unlimited
- Lecture 05 – Introduction to Artificial Intelligence: Philosophy Unlimited
- Lecture 06 – State Space Search: Introduction Unlimited
- Lecture 07 – Search: Depth First Search and Breadth First Search Unlimited
- Lecture 08 – Search: Depth First Iterative Deepening (DFID) Unlimited
- Lecture 09 – Heuristic Search Unlimited
- Lecture 10 – Hill Climbing Unlimited
- Lecture 11 – Solution Space Search, Beam Search Unlimited
- Lecture 12 – Travelling Salesman Problem (TSP) Greedy Methods Unlimited
- Lecture 13 – Tabu Search Unlimited
- Lecture 14 – Optimization I (Simulated Annealing) Unlimited
- Lecture 15 – Optimization II (Genetic Algorithms) Unlimited
- Lecture 16 – Population based Methods for Optimization Unlimited
- Lecture 17 – Population based Methods II Unlimited
- Lecture 18 – Branch and Bound, Dijkstra’s Algorithm Unlimited
- Lecture 19 – A* Algorithm Unlimited
- Lecture 20 – Admissibility of A* Unlimited
- Lecture 21 – A* Monotone Property, Iterative Deepening A* Unlimited
- Lecture 22 – Recursive Best First Search, Sequence Alignment Unlimited
- Lecture 23 – Pruning the Open and Closed Lists Unlimited
- Lecture 24 – Problem Decomposition with Goal Trees Unlimited
- Lecture 25 – AO* Algorithm Unlimited
- Lecture 26 – Game Playing Unlimited
- Lecture 27 – Game Playing – Minimax Search Unlimited
- Lecture 28 – Game Playing – Alpha-Beta Unlimited
- Lecture 29 – Game Playing – SSS* Unlimited
- Lecture 30 – Rule Based Systems Unlimited
- Lecture 31 – Inference Engines Unlimited
- Lecture 32 – Rete Algorithm Unlimited
- Lecture 33 – Planning Unlimited
- Lecture 34 – Planning FSSP, BSSP Unlimited
- Lecture 35 – Goal Stack Planning, Sussman’s Anomaly Unlimited
- Lecture 36 – Nonlinear Planning Unlimited
- Lecture 37 – Plan Space Planning Unlimited
- Lecture 38 – GraphPlan Unlimited
- Lecture 39 – Constraint Satisfaction Problems Unlimited
- Lecture 40 – Constraint Satisfaction Problems (cont.) Unlimited
- Lecture 41 – Knowledge Based Systems Unlimited
- Lecture 42 – Knowledge Based Systems, Propositional Logic Unlimited
- Lecture 43 – Propositional Logic Unlimited
- Lecture 44 – Resolution Refutation for Propositional Logic Unlimited
- Lecture 45 – First Order Logic (FOL) Unlimited
- Lecture 46 – Reasoning in First Order Logic (FOL) Unlimited
- Lecture 47 – Backward Chaining Unlimited
- Lecture 48 – Resolution for First Order Logic (FOL) Unlimited
Instructor
