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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: May be repeated in the same or separate terms if topics vary.

This course will introduce the key concepts and the state-of-the-art in practical, scalable, and fault-tolerant software systems for emerging Generative AI (GenAI). It will encourage you to design system tools or apply existing ones in your own research. Topics cover basics of GenAI models from a systems perspective; systems for GenAI lifecycle (pre-training, training, fine-tuning/alignment, inference serving, and grounding); etc. The course will be a mix of lectures, student presentations, seminar-style discussions, and a semester-long project on GenAI topics. Prerequisites: This course is NOT focused on AI methods but on building software systems to support AI methods in practice. Students are expected to have good programming skills and must have taken at least one undergraduate-level systems-related course (from operating systems, databases, distributed systems, or networking). Having an undergraduate ML/AI course is helpful but not required. For up-to-date information about CS c