Artificial Intelligence

Intelligence, Artificial Intelligence (AI), AI Perspectives: acting and thinking humanly, acting and thinking rationally, History of AI, Foundations of AI: Philosophy, Economics, Psychology, Sociology , Linguistics, Neuroscience, Mathematics, Computer Science, Control Theory, AI Ethics and Responsible AI : Bias and Fairness in AI , Transparency and Accountability, AI Regulations and Policies, Applications of AI.

Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents , Configuration of Agents, PEAS description of Agents, PAGE , Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Learning Agent , Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi -observable, Single Agent, Multi Agent

Definition, Problem as a state space search, Problem formulation, Well-defined problems, Solving Problems by Searching, Search Strategies, Performance evaluation of search techniques, Uninformed Search: 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, Adversarial search techniques, Mini -max Search, Alpha -Beta Pruning, Constraint Satisfaction Problems.

Definition and import ance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems , Types of Knowledge Representation Systems: Semantic Nets, Frames, Conceptual Dependencies, Scripts, Rule Based Systems( Production System), Propositional Logic, Predicate Logic, Propositional Logic(PL): Syntax, Semantics, Formal logic -connectives, truth tables, tautology, validity, well -formed-formula, Inference using Resolution, Backward Chaining and Forward Chaining , Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference with FOPL: By converting into PL (existential and universal instantiation), Unification and lifting, Inference using resolution , Handling Uncertain Knowledge, Radom Variables, Prior and Poster ior Probability, Inference using Full Joint Distribution, Bayes’ Rule and its use, Bayesian Networks, Reasoning in Belief Networks , Fuzzy Logic: Fuzzy Sets, Membership in Fuzzy Set, Fuzzy Rule base Systems.

Introduction to Machine Learning , Concepts of Learning, Supervised, Unsupervised and Reinforcement Learning , Statistical-based Learning: Naive Bayes Model , Learning by Genetic Algorithms: Operators in Genetic Algorithm: Selection, Mutation, Crossover, Fitness Function, Genetic Algorithm , Learning with Neural Networks: Introduction, Biological Neural Networks Vs. Artificial Neural Networks (ANN), Mathematical Model of ANN, Activation Functions: Linear, Step Sigmoid, Types of ANN: Feed -forward, Recurrent, Single Layered, Multi -Layered, Application of Artificial Neural Networks, Learning by Training ANN, Hebbian Learning, Perceptron Learning, Back -propagation Learning, Overview of Deep Learning Architectures : Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).

Expert Systems, Components of Expert System: Knowledge base, inference engine, user interface, working memory, Development of Expert Syste ms, Natural Language Processing: Natural Language Understanding and Natural Language Generation, Steps of Natural Language Processing: Lexical Analysis(Segmentation, Morphological Analysis), Syntactic Analysis, Semantic Analysis, Pragmatic Analysis, Machin e Translation, Machine Vision Concepts: Machine vision and its applications, Components of Machine Vision System, Object Detection and Recognition , Image Segmentation, Explainable AI in Computer Vision , AI in Healthcare and Bioinformatics, Applications of AI in Medicine, Predictive Modeling in Healthcare. Laboratory Works: Student should write programs and prepare lab sheet for most of the units in the syllabus. Majorly, students should practice design and imple mentation of intelligent agents, knowledge representation systems and machine learning techniques. Students are also advised to implement Neural Networks for solving practical problems of AI. Students are advised to use LISP, PROLOG, and any other high-level language like C, C++, Java, python etc.