Artificial Intelligence (Prof. Deepak Khemani)


Lecture 1 - Artificial Intelligence: Introduction


Lecture 2 - Introduction to AI


Lecture 3 - AI Introduction: Philosophy


Lecture 4 - AI Introduction


Lecture 5 - Introduction: Philosophy


Lecture 6 - State Space Search - Introduction


Lecture 7 - Search - DFS and BFS


Lecture 8 - Search DFID


Lecture 9 - Heuristic Search


Lecture 10 - Hill Climbing


Lecture 11 - Solution Space Search, Beam Search


Lecture 12 - TSP Greedy Methods


Lecture 13 - Tabu Search


Lecture 14 - Optimization - I (Simulated Annealing)


Lecture 15 - Optimization - II (Genetic Algorithms)


Lecture 16 - Population based methods for Optimization


Lecture 17 - Population Based Methods II


Lecture 18 - Branch and Bound, Dijkstra's Algorithm


Lecture 19 - A* Algorithm


Lecture 20 - Admissibility of A*


Lecture 21 - A* Monotone Property, Iterative Deeping A*


Lecture 22 - Recursive Best First Search, Sequence Allignment


Lecture 23 - Pruning the Open and Closed lists


Lecture 24 - Problem Decomposition with Goal Trees


Lecture 25 - AO* Algorithm


Lecture 26 - Game Playing


Lecture 27 - Game Playing - Minimax Search


Lecture 28 - Game Playing - AlphaBeta


Lecture 29 - Game Playing - SSS *


Lecture 30 - Rule Based Systems


Lecture 31 - Inference Engines


Lecture 32 - Rete Algorithm


Lecture 33 - Planning


Lecture 34 - Planning FSSP, BSSP


Lecture 35 - Goal Stack Planning. Sussman's Anomaly


Lecture 36 - Non-linear planning


Lecture 37 - Plan Space Planning


Lecture 38 - GraphPlan


Lecture 39 - Constraint Satisfaction Problems


Lecture 40 - CSP continued


Lecture 41 - Knowledge-based systems


Lecture 42 - Knowledge-based Systems, PL


Lecture 43 - Propositional Logic


Lecture 44 - Resolution Refutation for PL


Lecture 45 - First-order Logic (FOL)


Lecture 46 - Reasoning in FOL


Lecture 47 - Backward chaining


Lecture 48 - Resolution for FOL