Course Websites

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.

This course will introduce the basic concepts and cutting-edge practices in the design and implementation of efficient software systems for supporting machine learning (ML) models, with a particular focus on Generative AI (GenAI). By the end of the course, students will be able to: - Understand and critique the design principles behind state-of-the-art ML systems, from model architecture to system-level considerations. - Develop and utilize tools to profile, monitor, and optimize the performance of ML systems. - Explore and conduct research in topics related to the practical deployment and optimization of ML systems, contributing to the evolving landscape of efficient ML operations. Structure: The course will combine lectures, guest lectures from practioners, lab assignments, reading summaries, and a semester-long project. We will explore key ML topics from a systems perspective, addressing the relevant challenges across the ML lifecycle. Topics include, but are not limited to: - Basic