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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.

This course explores the cutting-edge intersection of large language models (LLMs) and machine reasoning, with a specific emphasis on their transformative potential in engineering disciplines. Modern LLMs, such as GPT, Claude, Gemini, and Llama, are foundation models with vast knowledge bases. These models have demonstrated significant potential in solving complex reasoning and coding tasks. But what do they offer engineers? This course addresses that question by examining LLM reasoning and its application to a range of engineering fields, including control systems, circuit design, power systems, signal processing, aerospace, and transportation engineering. Key topics include: How do LLMs function? How can they be leveraged for reasoning? What is the quality of LLM-generated reasoning for various engineering tasks? How much can we trust the engineering design solutions from LLMs? What are the fundamental limitations of LLM reasoning for engineering? What engineering benchmarks exist fo