Lecture 1 - Fuzzy Sets Fuzzy Membership Function Support Core Crossover points and Bandwidth of a Fuzzy set Lecture 2 - Fuzzy Sets and Membership Function Alpha-Cut of a Fuzzy Set Fuzzy subset and Scalar cardinality Lecture 3 - Fuzzy Sets and Membership Function Fuzzy subset and Scalar cardinality Basic operations and Properties Lecture 4 - Fuzzy Sets and Membership functions, intersection etc Lecture 5 - Fuzzy Sets Membership functions Basic operations Lecture 6 - Crisp Relation and Example Fuzzy Relation and operations Lecture 7 - Crisp relation and fuzzy relation Extension principle Fuzzy arithmetic numbers and operations Lecture 8 - Fuzzy relation and operations Fuzzy multi-valued logic Fuzzy implications Lecture 9 - Fuzzy relation and fuzzy implications Fuzzy multi-valued logic Defuzzification Lecture 10 - Defuzzification Lecture 11 - Fuzzy rules Generalised Modus Ponens Generalised Modus Tollens Lecture 12 - Fuzzy rules Fuzzy inference system Lecture 13 - Fuzzy inference system Mamdanis, Sugenos and Tsukamoto type inferences Lecture 14 - Fuzzy inference systems with examples Lecture 15 - Numerical examples Lecture 16 - Introduction of Genetic Algorithm Darwin’s Principle Lecture 17 - Working principle and flow chart of Genetic Algorithm Important terms of Genetic Algorithm Lecture 18 - Working procedure of Genetic Algorithm Initial population of Genetic Algorithm Lecture 19 - Basic Concepts of Genetic Algorithm Some encoding technique of Genetic Algorithm Lecture 20 - Working principle of Genetic Algorithm Population Set Encoding Techniques Lecture 21 - Selection operator Population diversity and Selection Pressure Exploration and Exploitation Lecture 22 - Population diversity and Selection Pressure Exploration and Exploitation Selection Methods Lecture 23 - Selection Methods and Crossover Techniques Lecture 24 - Selection Methods Crossover techniques Crossover Probability and Mutation Probability Lecture 25 - Crossover techniques Lecture 26 - Numerical Examples by using Genetic Algorithm Lecture 27 - Numerical Examples Lecture 28 - Numerical Examples (Continued...) Lecture 29 - Numerical Examples (Continued...) Lecture 30 - Numerical Examples (Continued...) Lecture 31 - Introduction of Multi-objective optimization Lecture 32 - Weighted Sum Method Epsilon-Constraint Method Lecture 33 - Weighted Distance Metric Method Lexicographic Ordering Method Reference point method Lecture 34 - Few Methods to solve problems of Multi-objective optimization Lecture 35 - Introduction of Multi objective GAsVEGA Method Lecture 36 - Vector Optimized Evolution Strategy (VOES) Weight-Based Genetic Algorithm (WBGA) Lecture 37 - NSGA and NSGA - II Lecture 38 - Numerical examples by using NSGA - II Lecture 39 - Concepts on non-domination Ranking Crowding distance operator NSGA - II Lecture 40 - Introduction of SPEA and SPEA 2 Lecture 41 - Introduction Rough Set theory Lecture 42 - Introduction Rough Set theory Indiscernibility Relation Lecture 43 - Indiscernibility Relation Lower Approximations Upper Approximations Lecture 44 - Lower Approximations Upper Approximations Boundary Regions Lecture 45 - Properties of Approximations and Basic classes of Rough Set theory with Numerical examples Lecture 46 - Rough membership function Dependency of Attributes Lecture 47 - Dependency of Attributes Reduct and Core Lecture 48 - Indispensable and Dispensable Attributes Reduct and Core Lecture 49 - Reduct and Core Discernibility Matrix and Function Lecture 50 - Numerical examples of Discernibility Matrix and Function Lecture 51 - Some concepts on Rough set theory Introduction to Rule Generation Lecture 52 - Rule Generation based on Reduct and Core Lecture 53 - Some Problems of Rule Generations Lecture 54 - Fuzzy-Rough Sets Lecture 55 - Some problems of Fuzzy-Rough sets Lecture 56 - Introduction to Artificial Neural Networks Lecture 57 - Numerical example of Backpropagation Algorithm Lecture 58 - Intelligent Systems Introduction to Neuro-Fuzzy system Lecture 59 - Adaptive Neuro-Fuzzy Systems Lecture 60 - Ant colony optimization techniques

Lecture 1 - Fuzzy Sets Fuzzy Membership Function Support Core Crossover points and Bandwidth of a Fuzzy set Lecture 2 - Fuzzy Sets and Membership Function Alpha-Cut of a Fuzzy Set Fuzzy subset and Scalar cardinality Lecture 3 - Fuzzy Sets and Membership Function Fuzzy subset and Scalar cardinality Basic operations and Properties Lecture 4 - Fuzzy Sets and Membership functions, intersection etc Lecture 5 - Fuzzy Sets Membership functions Basic operations Lecture 6 - Crisp Relation and Example Fuzzy Relation and operations Lecture 7 - Crisp relation and fuzzy relation Extension principle Fuzzy arithmetic numbers and operations Lecture 8 - Fuzzy relation and operations Fuzzy multi-valued logic Fuzzy implications Lecture 9 - Fuzzy relation and fuzzy implications Fuzzy multi-valued logic Defuzzification Lecture 10 - Defuzzification Lecture 11 - Fuzzy rules Generalised Modus Ponens Generalised Modus Tollens Lecture 12 - Fuzzy rules Fuzzy inference system Lecture 13 - Fuzzy inference system Mamdanis, Sugenos and Tsukamoto type inferences Lecture 14 - Fuzzy inference systems with examples Lecture 15 - Numerical examples Lecture 16 - Introduction of Genetic Algorithm Darwin’s Principle Lecture 17 - Working principle and flow chart of Genetic Algorithm Important terms of Genetic Algorithm Lecture 18 - Working procedure of Genetic Algorithm Initial population of Genetic Algorithm Lecture 19 - Basic Concepts of Genetic Algorithm Some encoding technique of Genetic Algorithm Lecture 20 - Working principle of Genetic Algorithm Population Set Encoding Techniques Lecture 21 - Selection operator Population diversity and Selection Pressure Exploration and Exploitation Lecture 22 - Population diversity and Selection Pressure Exploration and Exploitation Selection Methods Lecture 23 - Selection Methods and Crossover Techniques Lecture 24 - Selection Methods Crossover techniques Crossover Probability and Mutation Probability Lecture 25 - Crossover techniques Lecture 26 - Numerical Examples by using Genetic Algorithm Lecture 27 - Numerical Examples Lecture 28 - Numerical Examples (Continued...) Lecture 29 - Numerical Examples (Continued...) Lecture 30 - Numerical Examples (Continued...) Lecture 31 - Introduction of Multi-objective optimization Lecture 32 - Weighted Sum Method Epsilon-Constraint Method Lecture 33 - Weighted Distance Metric Method Lexicographic Ordering Method Reference point method Lecture 34 - Few Methods to solve problems of Multi-objective optimization Lecture 35 - Introduction of Multi objective GAsVEGA Method Lecture 36 - Vector Optimized Evolution Strategy (VOES) Weight-Based Genetic Algorithm (WBGA) Lecture 37 - NSGA and NSGA - II Lecture 38 - Numerical examples by using NSGA - II Lecture 39 - Concepts on non-domination Ranking Crowding distance operator NSGA - II Lecture 40 - Introduction of SPEA and SPEA 2 Lecture 41 - Introduction Rough Set theory Lecture 42 - Introduction Rough Set theory Indiscernibility Relation Lecture 43 - Indiscernibility Relation Lower Approximations Upper Approximations Lecture 44 - Lower Approximations Upper Approximations Boundary Regions Lecture 45 - Properties of Approximations and Basic classes of Rough Set theory with Numerical examples Lecture 46 - Rough membership function Dependency of Attributes Lecture 47 - Dependency of Attributes Reduct and Core Lecture 48 - Indispensable and Dispensable Attributes Reduct and Core Lecture 49 - Reduct and Core Discernibility Matrix and Function Lecture 50 - Numerical examples of Discernibility Matrix and Function Lecture 51 - Some concepts on Rough set theory Introduction to Rule Generation Lecture 52 - Rule Generation based on Reduct and Core Lecture 53 - Some Problems of Rule Generations Lecture 54 - Fuzzy-Rough Sets Lecture 55 - Some problems of Fuzzy-Rough sets Lecture 56 - Introduction to Artificial Neural Networks Lecture 57 - Numerical example of Backpropagation Algorithm Lecture 58 - Intelligent Systems Introduction to Neuro-Fuzzy system Lecture 59 - Adaptive Neuro-Fuzzy Systems Lecture 60 - Ant colony optimization techniques