Syllabus
Course Description
What does generative AI mean for the future of engineering practice? In this course, we will explore this question by building skills to understand and use generative AI effectively and responsibly, cultivate the practical wisdom to understand how to use it in our practice as engineers, and interrogate the societal impact of these tools. The course will be composed of a series of mini design projects that will teach you specific skills to connect physical sensors and actuators to the digital world and use modern software tools and AI to build these systems effectively. These projects will be followed by an open-ended course project where you will work as a solo founder to rapidly prototype a product that aligns with the College’s vision of STEM for a Better World.
Course Structure
The course has two phases:
Phase 1: Mini Design Projects (Weeks 1–7)
Six mini design projects across seven weeks, each paired with just-in-time instruction. Each week follows a consistent rhythm: Monday covers the core skill (with a pre-class video/reading), and Wednesday kicks off the project where you apply it.
| Lab | Topic | Skills |
|---|---|---|
| Lab 1 | Train a Neural Net | Code 1.0/2.0/3.0, Python, PyTorch, dev tools |
| Lab 2 | Build a Small Language Model | Large language models (LLMs), tokenization, embeddings |
| Lab 3 | Interactive Web App | HTML/CSS/JavaScript, DNS/HTTP, frontend |
| Lab 4 | Full-Stack App | Backend, application programming interfaces (APIs), databases |
| Lab 5 | Sensor-to-Cloud | Arduino, sensors, hardware-software integration |
| Lab 6 | Agentic AI Build | Agentic coding tools, systems integration |
A pre-assessment (Lab 0) on Day 2 and a post-assessment in Week 8 let you measure your own growth.
Phase 2: Final Project (Weeks 8–15)
You work as a solo founder to rapidly prototype a full product — hardware, software, and electrical — through a structured design cycle: user research, product requirements document (PRD), design reviews, build sprints, and a Demo Day presentation. See the Projects page for details.
Class Details
Grading
Course Kit & Materials
Collaboration Policy
AI Policy
Honor Code
All students are expected to uphold the Harvey Mudd College Honor Code. As stated in the code: “All members of ASHMC are responsible for maintaining their integrity and the integrity of the College community in all academic matters and in all affairs concerning the community.”
In this course, the Honor Code means:
- All submitted work must reflect your own understanding. Verbal collaboration with classmates is encouraged after you have given serious thought to each component yourself.
- All written submissions must be your own individual work, not copied or jointly written.
- Using software tools and internet resources is acceptable as long as it does not substitute for an understanding of the course material.
- Plagiarism and direct copying from online or any other sources is strictly prohibited.
- You may not reference assignments or solutions from previous semesters of this course.
If you are unsure whether something constitutes a violation, ask the instructor. Self-reporting of potential violations is encouraged for fair resolution.
Inclusiveness
This course is committed to creating a safe and supportive learning environment for all students, regardless of race, gender, ethnicity, sexual orientation, religion, and academic history. If you experience or witness a hostile environment in this course, please contact the instructor immediately.
Accessibility & Accommodations
Harvey Mudd College is committed to making all learning experiences accessible. If you anticipate or experience academic barriers related to a disability — including mental health conditions, chronic or temporary medical conditions — please contact the Office of Accessible Education at access@g.hmc.edu to establish reasonable accommodations.
Students from other Claremont Colleges should contact their home college’s disability resources office.