About This Course

MIT’s Artificial Intelligence course systematically explores knowledge representation, problem-solving, and learning approaches for designing and developing intelligent systems. It provides students with both a theoretical and practical framework for understanding the foundational concepts they will encounter in the field of artificial intelligence.

Topics such as knowledge representation and inference, search algorithms, decision trees, Bayesian reasoning, artificial neural networks, genetic algorithms, and reinforcement learning are presented through real-world scenarios and illustrative applications. This structure enables students to view AI not only as an algorithmic tool, but also as a research discipline aimed at understanding human intelligence.

By the end of the course, students gain the ability to build intelligent systems for concrete computational problems, understand the mechanisms behind learning systems, and evaluate cognitive processes from a computational perspective.

Instructor

Prof. Patrick Henry Winston served for many years as a faculty member in MIT’s Department of Electrical Engineering and Computer Science. A pioneering academic in the field of artificial intelligence, he is especially known for his work on knowledge representation, visual perception, and cognitive models. Renowned for his passion for teaching, Winston transformed course 6.034 into one of MIT’s most inspiring AI classes. He earned deep appreciation from students for both his scholarly depth and his intuitive, engaging instructional style.