Build a practical foundation in Artificial Intelligence and Machine Learning while learning how intelligent systems search, reason, learn, and make decisions. You will begin with AI concepts, intelligent agents, state space representation, and problem-solving through BFS, DFS, and backtracking. You will then apply heuristic search, hill climbing, best-first search, minimax, and alpha-beta pruning to structured and adversarial problems.

The course advances into machine learning fundamentals, including perceptrons, neural networks, backpropagation, k-means clustering, and supervised and unsupervised learning. You will also use propositional and predicate logic, inference rules, unification, Skolemization, resolution, and Prolog to represent knowledge and solve logical problems. Practical CLIPS tutorials guide you from basic rules to templates, variables, wildcards, quantifiers, and logical operators for building expert systems. Designed for learners seeking both conceptual understanding and hands-on AI practice, this course concludes with intelligent agent architectures, reinforcement learning, Markov Decision Processes, and Bayesian reasoning for decision-making under uncertainty. Its distinctive progression connects classical AI search, machine learning, symbolic reasoning, expert systems, and probabilistic models, helping you apply AI and ML techniques to problems in research, business, and technology.