9. Artificial Intelligence and Neural Networks

9.1 Introduction to AI and Intelligent Agent

  • Concept of Artificial Intelligence (AI)

  • AI Perspectives

  • History of AI and Applications

  • Foundations of AI

  • Introduction to Agents

    • Structure of Intelligent Agent

    • Properties of Intelligent Agents

    • PEAS Description of Agents

  • Types of Agents:

    • Simple Reflexive, Model-Based, Goal-Based, Utility-Based

  • Environment Types:

    • Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi-Agent

9.2 Problem Solving and Searching Techniques

  • Problem Formulation and State Space Search

    • Well-defined Problems, Constraint Satisfaction Problem

  • Uninformed Search Techniques:

    • Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Bidirectional Search

  • Informed Search:

    • Greedy Best First Search, A* Search, Hill Climbing, Simulated Annealing

  • Game Playing and Adversarial Search:

    • Mini-max Search, Alpha-Beta Pruning

9.3 Knowledge Representation

  • Knowledge Representation and Mappings

    • Approaches to Knowledge Representation

    • Issues in Knowledge Representation

  • Semantic Nets and Frames

  • Propositional Logic (PL):

    • Syntax, Semantics, Formal Logic-Connectives, Tautology, Validity, Well-Formed Formula, Inference Using Resolution

  • Predicate Logic (FOPL):

    • Syntax, Semantics, Quantification, Rules of Inference, Unification, Resolution Refutation System

  • Bayesian Networks:

    • Bayes' Rule, Reasoning in Belief Networks

9.4 Expert Systems and Natural Language Processing

  • Expert Systems:

    • Architecture of Expert Systems, Knowledge Acquisition, Declarative vs Procedural Knowledge, Development of Expert Systems

  • Natural Language Processing (NLP):

    • Terminology, Natural Language Understanding and Generation, Steps of NLP

    • Applications and Challenges of NLP

  • Machine Vision and Robotics:

    • Concepts, Stages of Machine Vision, Robotics

9.5 Machine Learning

  • Introduction to Machine Learning

  • Learning Concepts:

    • Supervised, Unsupervised, and Reinforcement Learning

  • Types of Learning:

    • Inductive Learning (Decision Tree), Statistical-Based Learning (Naive Bayes Model), Fuzzy Learning

  • Fuzzy Inference System and Methods

  • Genetic Algorithm:

    • Genetic Algorithm Operators, Encoding, Selection Algorithms, Fitness Function, and Genetic Algorithm Parameters

9.6 Neural Networks

  • Biological vs. Artificial Neural Networks (ANN)

  • McCulloch-Pitts Neuron and Mathematical Model of ANN

  • Activation Functions

  • Neural Network Architectures:

    • Perceptron, Learning Rate, Gradient Descent, Delta Rule, Hebbian Learning

    • Adaline Network, Multilayer Perceptron Neural Networks, Backpropagation Algorithm, Hopfield Neural Network

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