Artificial Intelligence & Machine Learning

Introduction, historical development, foundation areas, tasks and application areas. Intelligent Agents: Introduction, types and structure of intelligent agents. Computer Vision, Natural language processing.

Introduction, Problem solving by searching, Searching for solutions, Uniformed and Informed searching techniques. Local search algorithms, Adversarial search methods, Search techniques used in games, Alpha-Beta pruning.

Propositional logic, Predicate logic, First order logic, Inference in first order logic, Clause form conversion, Resolution. Chaining: Concept, forward chaining and backward chaining, Utility theory and Probabilistic reasoning.

Introduction, types, components of learning process, issues, application areas and performance measurement criteria of learning algorithm, Data preprocessing, Data transformation, Scaling training and test data. Supervised Learning: Introduction, operations, types of learning algorithms, Regression, Classification algorithms – Decision tree, Support vector machine, K-Nearest Neighbors, Naïve Bayes.

Unsupervised learning: Introduction, types, working, issues, application areas, clustering algorithms (K-means, Hierarchical), Association rule learning and dimensionality reduction. Reinforcement learning: Introduction, components, types, working, issues and application areas.