Graduate Artificial Intelligence Course ID 15780 Description This course provides a broad perspective on AI, with a focus on foundational principles powering modern AI. This course will cover (i) machine learning and neural networks, (ii) large language models and generative AI, (iii) search and reinforcement learning, (iv) game theory and multi-agent systems, and (v) issues of bias and unfairness in AI. The material will be presented from a mathematical perspective, with assignments emphasizing implementation alongside foundational principles. Key Topics (i) classical approaches of search and planning useful for robotics, (ii) integer programming and continuous optimization that form the bedrock for many AI algorithms, (iii) modern machine learning techniques including deep learning that power many recent AI applications, (iv) game theory and multi-agent systems, and (v) issues of bias and unfairness in AI. Required Background Knowledge There are no formal pre-requisites for the course, but students should have previous programming experience (programming assignments will be given in Python), as well as some general CS background. Please see the instructors if you are unsure whether your background is suitable for the course. Course Relevance This course is targeted at graduate students who are interested in learning about artificial intelligence. Course Goals In addition to understanding the theoretical foundations, we will also study modern algorithms in the research literature. Learning Resources Piazza, Gradescope, courses website Assessment Structure 45% homework, 20% midterm, 30% course project, 5% participation Course Link http://www.cs.cmu.edu/~15780/
