By the end of this course, learners will be able to design intelligent agents, apply search algorithms, implement machine learning models, perform logical reasoning, build expert systems with CLIPS, and apply probabilistic models for decision-making. The course equips participants with a strong foundation in Artificial Intelligence and Machine Learning, combining theory with hands-on practice.

This training begins with AI fundamentals, intelligent agents, and search strategies, then advances to heuristic methods and game-playing algorithms. Learners will explore neural networks, backpropagation, and clustering to understand machine learning essentials. Logical reasoning and knowledge representation are introduced through propositional and predicate logic, unification, resolution, and Prolog programming. Expert systems are covered in depth with practical CLIPS tutorials, progressing from basics to advanced features. Finally, the course integrates intelligent agent architectures with reinforcement learning, Markov Decision Processes, and Bayesian reasoning to manage uncertainty. Unique to this course is its balance of conceptual clarity and practical exercises, ensuring learners gain both the “why” and the “how” of AI. By completing this course, learners will be well-prepared to apply AI and ML techniques to solve real-world problems in research, business, and technology.