Syllabus

Introduction to AI and intelligent agent

Concept of Artificial Intelligence, AI Perspectives, History of AI, Applications of AI, Foundations of AI, Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents, PEAS description of Agents, Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based; and Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent.

Problem solving and searching techniques

Definition, Problem as a state space search, Problem formulation, 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, Adversarial search techniques, Mini-max Search, and Alpha-Beta Pruning.

Knowledge representation

Knowledge representations and Mappings, Approaches to Knowledge Representation, Issues in Knowledge Representation, Semantic Nets, 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), Bayes' Rule and its use, Bayesian Networks, and Reasoning in Belief Networks.

Expert system and natural language processing

Expert Systems, Architecture of an expert system, Knowledge acquisition, Declarative knowledge vs Procedural knowledge, Development of Expert Systems, Natural Language Processing Terminology, Natural Language Understanding and Natural Language Generation, Steps of Natural Language Processing, Applications of NLP, NLP Challenges, Machine Vision Concepts, Machine Vision Stages, and Robotics.

Machine learning

Introduction to Machine Learning, Concepts of Learning, Supervised, Unsupervised and Reinforcement Learning, Inductive learning (Decision Tree), Statistical-based Learning (Naive Bayes Model), Fuzzy learning, Fuzzy Inferences System, Fuzzy Inference Methods, Genetic Algorithm (Genetic Algorithm Operators, Genetic Algorithm Encoding, Selection Algorithms, Fitness function, and Genetic Algorithm Parameters).

Neural networks

Biological Neural Networks Vs. Artificial Neural Networks (ANN), McCulloch Pitts Neuron, Mathematical Model of ANN, Activation functions, Architectures of Neural Networks, The Perceptron, The Learning Rate, Gradient Descent, The Delta Rule, Hebbian learning, Adaline network, Multilayer Perceptron Neural Networks, Backpropagation Algorithm, Hopfield Neural Network.