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    • Introduction of AI

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      AI is the branch of computer science; which deals with building intelligent agents(System), which can be able o solve the problem, and evolve by themselves.

                                                                                     Agenda:




      • Natural intelligence
      • What is AI
      • Major AI areas.
      • History of AI

    • Lecture #2:Intelligent Agents

      • Intelligent Agents
      • Agent and environments
      • Rationality
      • PEAS(performance, measure , environment, actuators, sensors)
      • An intelligent agent is a program or software which perceives(or observes or senses) its environment through sensors, thinks intelligently and act upon that environment through its actuators


    • Intelligent Agent Types

      ◦Simple reflex agent
      ◦Model based reflex agent
      ◦Goal based agent
      ◦Utility Based Agents
      ◦Learning agents

    • Problem solving through uninformed search

      • Breadth First Search (BFS)
      • Depth First search (DFS)
      • Difference between BFS and DFS

    • Problem solving through informed search

      • Heuristically  informed method
      • Hill Climbing 
      • Beam Search

    • Expert System

      • Introduction to Expert Systems 
      • knowledge base expert system their type and application 
      • Working of expert system with its different components 
      • Typical example. It benefits, the downside 
      • Developing an expert system. Identifying the problem

    • Artificial Neural Network (ANN)

      • Introduction to Artificial Neural Network (ANN)
      • ANN application 
      • Topologies of ANN
      • Single Layer perception (SLP) Back pro algorithms
      • multilayer perception  (mlp) Back pro algorithms
      • Neural Network 
      • implementation for AND, OR, XOR 
      • Back Prop algorithm: Multilayer Perception, back prop algorithm
      • Neural Network: implementation for AND, OR, XOR


    • Genetic Algorithm (GA)

      • Introduction to evolutionary computing GA.
      • Application of GA and its examples 
      • Genetic operators (Crosse over and Mutation) and its applications
      • GA Pseudo Code example 

    • Particle Swarm Optimization (PSO)

      • Introduction to Swarm Intelligence (SI) , PSO
      • PSO Pseudocode and example 


    • Pattern Recognition

      • Machine Learning: Introduction to learning: Supervised learning 
      • Machine Learning: Unsupervised Learning, K-means Clustering algorithm 

    • Advance topic

      Advance topic in AI

    • Matlab

      • Basic AI concepts in MATLAB
      • MATLAB Toolbox relevant to AI
      • Practical work in MATLAB

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