Yuchou Chang, PhD

Foundations of artificial intelligence. The course introduces core concepts including agents, search, logic, planning, reasoning, learning, and inference. It examines how these methods support intelligent behavior and problem solving. The course connects these ideas to modern approaches such as foundation models, large-scale neural networks, and transformer-based architectures. Cutting-edge AI agents illustrate how modern systems integrate perception, reasoning, planning, and tool use in complex environments. Conceptual understanding and practical skills are developed through hands-on, industry-relevant projects involving AI systems and applications.

Expert system architectures: forward-production, logic programming, deductive retrieval, and semantic network systems. The course also treats natural language systems, illustrative working systems, and AI programming.

Theories and models in visual analytics. Visual analytics covers cognitive theories, advanced computational models and usable visual interface for sensemaking of data. This course is intended for graduate students interested in using visualizations for data analytics in their own work, or students interested in building visual analytics tools.

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.