Course Detail (Course Description By Faculty)

This course is designed to introduce students to the cutting-edge field of Artificial Intelligence (AI). Across detailed lectures, participants will gain insights into the core principles of AI and machine learning, explore the intricacies of natural language processing, discover the potential of vision recognition and image generation technologies, delve into reinforcement learning for sequential decision-making, and learn about the design of recommender systems that power personalized user experiences. The course also examines the burgeoning field of generative AI and the AI-generated content industry, while emphasizing the crucial importance of bias assessment and the principles of responsible AI.

This course is designed for those who are interested in the inner workings of state-of-the-art AI technologies. Specifically, it places emphases on the philosophy and intuition behind these technologies, as well as their promises and perils, but not on the technical details.

View welcome/overview video.

There are no formal prerequisites for this course. However, the material covered draws on a range of background knowledge in economics, statistics, and finance. While MBA core courses are recommended, they are not required. Cannot enroll in 43100 if 32200 taken previously.  

Canvas site. There is no required text for this course, as the provided lecture notes and cases are self-contained.

Grades will be determined as follows: six homework assignments (30%), participation and professionalism (20%), and an individual final exam (50%). For the homework assignments, you are encouraged to work in groups of up to four students, with each group submitting a single, well-organized and professionally presented write-up. Homework is due at the start of each class. The final exam will be a take-home assignment, similar in style to the homework, but must be completed individually.

Restrictions