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About The Course 0
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Description 1
This course enables learning on different graph traversal techniques (BFS & DFS)
along with enhanced search algorithms like A* algorithm. Genetic algorithms are discussed along with Min-Max algorithms.
Expert systems and ANN are also discussed in detail along with Fuzzy logic in SC.
Along with a pdf with important notes and explanations
Modules Covered:
Introduction to AI / SC
Problem solving algorithms
Knowledge, Reasoning and Planning.
Fuzzy Logic.
Artificial Neural Network.
Expert System.-
Lecture2.1
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How to Pass AISC 1
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Lecture3.1
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Introduction to Artificial Intelligence(AI) and Soft Computing 5
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Lecture4.1
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Lecture4.2
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Lecture4.3
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Lecture4.4
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Lecture4.5
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Problem Solving 10
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Lecture5.1
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Lecture5.2
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Lecture5.3
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Lecture5.4
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Lecture5.5
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Lecture5.6
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Lecture5.7
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Lecture5.8
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Lecture5.9
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Lecture5.10
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Fuzzy Logic 6
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Lecture6.1
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Lecture6.2
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Lecture6.3
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Lecture6.4
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Lecture6.5
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Lecture6.6
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Knowledge, Reasoning and Planning 6
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Lecture7.1
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Lecture7.2
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Lecture7.3
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Lecture7.4
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Lecture7.5
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Lecture7.6
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Artificial Neural Network 7
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Lecture8.1
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Lecture8.2
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Lecture8.3
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Lecture8.4
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Lecture8.5
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Lecture8.6
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Lecture8.7
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Expert System 4
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Lecture9.1
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Lecture9.2
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Lecture9.3
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Lecture9.4
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Notes 6
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Lecture10.1
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Lecture10.2
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Lecture10.3
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Lecture10.4
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Lecture10.5
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Lecture10.6
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Extra Notes 9
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Lecture11.1
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Lecture11.2
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Lecture11.3
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Lecture11.4
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Lecture11.5
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Lecture11.6
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Lecture11.7
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Lecture11.8
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Lecture11.9
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Introduction to Fuzzy Logic
Introduction to Fuzzy Logic
Fuzzy logic is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by Lotfi Zadeh of the University of California at Berkeley in the 1960s. Fuzzy Logic is defined as a many-valued logic form that may have truth values of variables in any real number between 0 and 1. It is the handle concept of partial truth. In real life, we may come across a situation where we can’t decide whether the statement is true or false.
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