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Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 0 to 4 undergraduate hours. 0 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.

In this course, we focus on teaching students practical system-building skills and toolchains for developing and fine-tuning large language models (LLMs) from scratch. It covers the entire lifecycle of developing an LLM and its enabled applications, including LLM architecture implementation, model training, fine-tuning, inference, and agentic-based applications. Along with the presentation of the entire LLM development lifecycle, we will not only discuss the basic building blocks of foundation models, but also learn the essential computing techniques and engineering skills needed for enabling functionality, efficiency, and scalability. Students will have multiple programming assignments to strengthen their understanding of the basic concepts and practice their systems-building skills. Specifically, the lecture topics will include PyTorch and development tools (software setup), CUDA implementation of tensor operators, AI infrastructure (hardware setup), the basics of foundation models (