Overview
Description
Foundations of generative AI and machine learning for engineering applications. Topics include neural networks, large language models, prompt engineering, fine-tuning, and multimodal AI. Students gain hands-on experience applying AI tools to engineering problem-solving, data analysis, and design. Emphasis on ethical use, critical evaluation, and discipline-specific capstone projects.
Requirements
Prerequisites
- instructor’s permission
CS 601
Original catalog text
Prerequisites
Prerequisite(s): CS 601 or instructor’s permission
Units
Lecture3
Catalog Details
Offering
Offered: Every Fall and Spring
Terms
fall, spring
Restrictions
Open to all engineering disciplines at the undergraduate and graduate level.
Attributes
Standard
Learning Outcomes
- identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
- apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
- develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
- function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.
- apply research methodology.