#1
6th June 2015, 08:56 AM
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AI Notes IIT Kharagpur
Hello ! sir kindly provide the the notes of IIT Kharagpur for the subject of AI as soon as possible ???? Reply soon???
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#2
6th June 2015, 10:26 AM
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Re: AI Notes IIT Kharagpur
Hello ! as you want the notes of IIT Kharagpur of the AI subject so here I am attaching an pdf file for you … and for more information I am providing you the detailed syllabus for AI Module 1: Introduction to Artificial Intelligence Lesson 1 Intro to AI: What is AI ? Examples of AI systems. Approaches to AI. Brief history of AI Lesson 2 Intelligent Agent : stimulus-response agents. components of intelligence Module 2: Problem Solving using Search - Single agent search Lesson 3 Introduction to State Space Search Statement of Search problems: state space graphs. Searching explicit state spaces. Feature based state spaces. Problem types, examples (puzzle problem, n-queen, the road map, traveling salesman, etc.) Lesson 4 Uninformed Search: Formulating the state space. Greedy search, breadth-first, depth- first, iterative deepening, bidirectional search Lesson 5 Informed Search Strategies I - Using evaluation functions. A general graph-searching algorithm. Uniform cost search, A*, admissibility of A* Lesson 6 Informed Search Strategies II - Iterative deepening A*, recursive best first search Module 3: Problem Solving using Search -Two agent search Lesson 7 Adversarial search: Two agent games. Minimax Lesson 8 Two agent games : alpha beta pruning Module 4: Constraint satisfaction problems Lesson 9 Constraint satisfaction problems - I Definitions, examples, constraint-graph, backtracking, forward checking, constraint propagation (arc-consistency, path-consistency) Lesson 10 Constraint satisfaction problems II dynamic ordering, incremental repair (min-conflicts heuristic), CSP and SAT, GSAT Module 5: Knowledge Representation and Logic - Propositional Logic Lesson 11 Propositional logic, syntax, semantics, semantic rules, terminology - validity, satisfiability. interpretation, entailment, proof systems Lesson 12 Propositional Logic inference rules, natural deduction, propositional resolution Module 6: Knowledge Representation and Logic - First Order Logic Lesson 13 First Order Logic - I Motivation, Syntax, Interpretations, semantics of quantifiers Lesson 14 First Order Logic - II Entailment in FOL, Interpretation Lesson 15 Inference in FOL - I First Order resolution. Conversion to clausal form. Lesson 16 Inference in FOL - II Unification. Most general unifier. Resolution with variables Proving validity Module 7: Knowledge Representation and Logic - Rule based Systems Lesson 17 Rule Based Systems - I Forward chaining. Backward chaining. Conflict resolution Lesson 18 Rule Based Systems - II Module 8: Other representation formalisms Lesson 19 Semantic nets Lesson 20 Frames - I Lesson 21 Frames - II Module 9: Planning - 4 lectures Lesson 22 Logic based planning situation calculus, frame problem Lesson 23 Planning systems : Describing states and goals. STRIPs. regression planning Lesson 24 Planning algorithm - IL25. Planning algorithm - II Module 10: Reasoning with uncertainty - Probabilistic reasoning Lesson 26 Reasoning with uncertain information Review of Pobability Theory Lesson 27 Probabilistic Inference Lesson 28 Bayes Network Lesson 29 A basic idea of inferencing with Bayes networks Module 11: Reasoning with uncertainty - Fuzzy Reasoning Lesson 30 Other paradigms of uncertain reasoning. Introduction to Fuzzy sets Lesson 31 Fuzzy set representation. Fuzzy inferences Lesson 32 Fuzzy reasoning - continued Module 12: Machine Learning Lesson 33 Learning : introduction Lesson 34 Learning from Observations. Lesson 35 Rule induction and Decision Tree - I Lesson 36 Rule induction and Decision Trees - II Lesson 37 Learning and Neural Networks - I Lesson 38 Neural Networks - II Lesson 39 Neural Networks - III Module 13: Natural Language Processing Lesson 40 Issues in NLP. Natural language understanding Lesson 41 Parsing. Natural language generation And you can also download this pdf file for the syllabus of AI |