This module introduces the fundamentals of Artificial Intelligence, including definitions, intelligent agents, and state space search. Learners will explore basic search algorithms such as BFS, DFS, and backtracking, gaining a strong foundation in AI problem-solving techniques.

This module covers heuristic-based search techniques and adversarial game strategies. Learners will examine heuristic functions, admissibility, hill climbing, best-first search, and the minimax algorithm with alpha-beta pruning.

This module introduces the basics of machine learning with a focus on perceptrons, neural networks, backpropagation, and clustering algorithms. Learners will gain hands-on understanding of supervised and unsupervised learning methods.

This module explores symbolic reasoning, covering propositional and predicate logic, inference rules, unification, resolution, and Prolog programming. Learners will also analyze reasoning frameworks such as case-based and model-based reasoning.

This module introduces rule-based expert systems with practical applications using the CLIPS programming environment. Learners will progress from CLIPS basics to advanced features such as variables, templates, wildcards, and quantifiers.

This module integrates intelligent agent architectures with decision-making frameworks, reinforcement learning, and probabilistic models. Learners will explore MDPs, Bayesian reasoning, and strategies for handling uncertainty in AI systems.