Course Contents
Course Contents
Unit I - Introduction to AI
Definitions - Foundation and History of Al, Evolution of Al - Applications of Al, Classification of Al systems with respect to environment. Artificial Intelligence vs Machine learning, Statistical Analysis: Relationship between attributes: Covariance, Correlation Coefficient, Chi Square. Intelligent Agent: Concept of Rationality, nature of environment, structure of agents.
Unit Il - Problem Solving
Heuristic Search Techniques: Generate-and-Test; Hill Climbing; Properties of A* algorithm, Bestfirst Search; Problem Reduction. Constraint Satisfaction problem: Interference in CSPs; Back tracking search for CSPs; Local Search for CSPs; structure of CSP Problem. Beyond Classical Search: Local search algorithms and optimization problem, local search in continuous spaces, searching with nondeterministic action and partial observation, online search agent and unknown environments.
Unit III - Knowledge and Reasoning
Knowledge and Reasoning: Building a Knowledge Base: Propositional logic, first order Logic, situation calculus. Theorem Proving in First Order Logic, Planning, partial order planning. Uncertain Knowledge and Reasoning, Probabilities, Bayesian Networks. Probabilistic reasoning over time: time and uncertainty, hidden Markova models, Kalman filter, dynamic bayesian network, keeping track of many objects
Unit IV Learning
Learning from examples: Overview of different forms of learning, Supervised learning, Unsupervised learning, Learning Decision Trees, regression and classification with linear model, SVM, Ensemble learning, Reinforcement learning. Artificial neural network
Unit V Game
Search under adversarial circumstances. Optimal decision in game, minimax algorithm, alpha-beta pruning, games with an element of chance, imperfect real time decision, stochastic games, partially observable games, stat of art game program, alternative approaches
Unit VI Expert Systems
Introduction to Expert Systems- Inference - Forward chaining - Backward chaining - Languages and tools - Explanation facilities - Knowledge acquisition. Applications: Natural Language Processing: General framework for text processing. Case Study: Sentiment Analysis. Computer Vision: General framework for CV application. Case Study: Object Recognition
Link to original note: Honours - Artificial Intelligences and Machine LearningHonours - Artificial Intelligences and Machine LearningCourse Objectives Course Outcomes Course Contents AI-Notes Learning Resources