GA_5_4_Neural Network Python Code

GA_5_3_Neural Network

GA_5_1_Travelling salseman

GA_4_3_Types of Mutation

GA_4_1_Types of crossover

GA_3_3_Types of Selestion Methods

GA_3_2_Encoding implementation

GA_3_1_Types of Encoding

GA_2_3_Python code for Genetic Algorithm

GA_2_2_Working of Genetic Algorithm with example

GA_2_1_Process of Genetic Algorithm

GA_1_1 Overview of the course GA

GA_1_2 History of Genetic Algorithm

GA_1_3_Terminologies in Genetic Algorithm

TOC – DFA for L = { w over {0,1} | w is a Binary String Divisible by 5 } | Deeba Kannan

TOC – DFA for L = { w over {0,1} | w is a Binary String Divisible by 4 } | Deeba Kannan

TOC – DFA for L = { w over {0,1} | w is a Binary String Divisible by 3 } | Deeba Kannan

Artificial Intelligence – Dempster-Shafer Theory in AI by Deeba Kannan

Artificial Intelligence - Expert system Architecture by Deeba Kannan

Artificial Intelligence - Expert System Introduction by Deeba Kannan

Artificial Intelligence - Planning Languages by Deeba Kannan

Artificial Intelligence - Means Ends Analysis by Deeba Kannan

Artificial Intelligence - Block World Problem by Deeba Kannan

Artificial Intelligence - Simple planning agent by Deeba Kannan

Artificial Intelligence - Planning introduction by Deeba Kannan

Artificial Intelligence - Knowledge Representation using Frames

Artificial Intelligence - Knowledge Representation using Semantic Nets

Artificial Intelligence - Bayesian Belief Network (BBN)

Artificial Intelligence - Predicate Resolution Example

Artificial Intelligence - Unification and Lifting

Artificial Intelligence - Application of A star in 8 puzzle problem

Artificial Intelligence - Best First Search - 8 Puzzle Problem

Artificial Intelligence - Best First Search

Relationship between PCP, MPCP and Turing Machine by Deeba Kannan

Theory of Computation – MPCP to PCP by Deeba Kannan

Theory of Computation – Closure Properties of Regular Languages by Deeba Kannan

Theory of Computation – Parsing Introduction by Deeba Kannan

Theory of Computation – Closure Properties of Context-Free Languages by Deeba Kannan

Theory of Computation – Scenario-Based Question in GNF Conversion by Deeba Kannan

Theory of Computation – Example 2: Greibach Normal Form by Deeba Kannan

Theory of Computation – Example 2: Chomsky Normal Form by Deeba Kannan

Theory of Computation – Scenario-Based Example for CFG, Parse Tree, and CNF by Deeba Kannan

Theory of Computation – Context-Free Grammar for Regular Expressions by Deeba Kannan

Theory of Computation – Context-Free Grammar for Programming Language Syntax by Deeba Kannan

Theory of Computation – Context-Free Grammar for Context-Free Language by Deeba Kannan

Theory of Computation – Moore to Mealy Machine Conversion by Deeba Kannan

Theory of Computation – Mealy to Moore Machine Conversion by Deeba Kannan

Theory of Computation – Mealy Machine Working and Representation with Examples by Deeba Kannan

Theory of Computation – Moore Machine Working and Representation with Example by Deeba Kannan

Theory of Computation – Introduction to Moore and Mealy Machines by Deeba Kannan

Artificial Neural Networks – Self Organizing Map Example by Deeba Kannan

Artificial Neural Networks – Self Organizing Map Working and Algorithm by Deeba Kannan

Artificial Neural Networks – Learning Vector Quantization Example by Deeba Kannan

Artificial Neural Networks - Learning Vector Quantization (LVQ) Algorithm and working

Artificial Intelligence – Types of Agent-Based Problems by Deeba Kannan

Artificial Intelligence – Types of Environment in Agent-Based Problems by Deeba Kannan

Artificial Neural Network – Perceptron Example by Deeba Kannan

Artificial Neural Network – Perceptron Algorithm by Deeba Kannan

Artificial Neural Network – Hebb Nets Example 2 by Deeba Kannan

Artificial Neural Network – Hebb Nets Example 1 by Deeba Kannan