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 consists of a series of labs in which you will implement components in an AI model to see how such models work, and then see how such models can be used to rapidly build tools that interface with our digital world. These labs will be followed by an open-ended course project where you will work independently to rapidly prototype a product that aligns with the College’s vision of STEM for a Better World.
Course Logistics
| Course | ENGR 190BO HM-01 |
| Instructors | TJ Tsai, Josh Brake |
| Meeting time | Monday & Wednesday, 9:35–10:50 am |
| Location | Beckman Hall B126 |
| Credits | 3 (HMC) · 1 (non-HMC) |
| Prerequisite | ENGR080 HM |
| Office hours | TBD |
Where to go
| Platform | Use it for | Link |
|---|---|---|
| Course website | Syllabus and schedule | course.aiprototypes.org |
| Canvas | Submitting labs; grades | Canvas course |
| Zulip | Questions and discussion | Join the course Zulip |
| YouTube | Pre-class videos | Course channel |
| Glassroom AI | Course AI chatbots for labs and pre-class assessments | TBD |
| Check-off sheet | Signing up for lab check-in slots | TBD |
Learning Goals
By the end of this course you should be able to:
- Understand the architecture and general operating principles of large language models (LLMs) and how they fit into the computing landscape.
- Gain insight into how these tools work in practice through hands-on implementation, with a focus on their capabilities and limitations and the reasons for these.
- Reflect critically on your learning processes and develop effective strategies for using AI as a learning tool that deepens rather than bypasses your understanding of engineering practice.
- Effectively collaborate with AI tools to extend your abilities in engineering disciplines, creating functional products or systems that demonstrate design thinking and have real-world applications.
- Critically examine and articulate the environmental, societal, and ethical impacts of AI technology through research, reflection, and creative communication, demonstrating understanding of both beneficial and harmful applications.
Course Structure
The full course schedule lists all dates and due dates. The course has two phases:
Phase 1: Labs (Weeks 1–8)
In the first part of the course, we will start from foundational principles and build components of modern AI tools to understand how they work under the hood. We will work our way up the stack: once we build a component, we will abstract it away and move to the next layer of abstraction. Our end goal is that students are equipped to use AI effectively and responsibly, rooted in a deep understanding of how modern AI systems work.
| Lab | Topic | Skills |
|---|---|---|
| Lab #0 | Setup | Python and programming refresh |
| Lab #1 | Gradient Descent | Use gradient descent to fit a linear regression model |
| Lab #2 | Neural Network | Implement a dense neural network from scratch in numpy |
| Lab #3 | Language Model | Train a small language model in Python |
| Lab #4 | Agentic AI | Implement a basic harness that calls tools |
| Lab #5 | Backends | Servers, APIs, and databases |
| Lab #6 | Frontends | Design web interfaces |
| Lab #7 | Full-Stack Web App Deployment | Put it all together |
A pre-assessment in week 1 and post-assessment later in the course will help you measure your own growth.
Phase 2: Final Project (Weeks 9–15)
For the final project you will work independently on a project you propose. As part of the project process, you will give and receive feedback from a small group of your peers as you prototype and build a product which integrates AI using the tools you’ve learned about and skills you’ve developed in this class. More information about the final project will be provided as we get closer to launching it.
Class Details
In the first part of the course, each week will consist of the following activities:
- Videos. Before each class, students will be expected to watch one or more videos to prepare for the class activities.
- Quiz prep. Before each in-class quiz there will also be a quiz prep activity, in which you will interact with a chatbot to verify your understanding of key learning outcomes.
- Class time. Classes will primarily consist of interactive activities and quizzes.
- Labs. Labs are generally programming assignments in which you will implement components of an AI system. They are intended to be done with assistance from dedicated chatbots in Glassroom AI. Labs will be submitted on Canvas and are due on Wednesdays at midnight. Each submission has two parts: a one-page executive summary — what you did, what you learned, and what questions you have — and evidence of your work.
- Lab check-ins. Every week, each student will have a 1-on-1 10 minute lab check-in with an instructor. These will be held on Thursdays and Fridays. An instructor will ask some targeted questions about the (recently submitted) lab to assess how well the student has met the desired learning outcomes. Note that the student’s grade component for labs is primarily from the check-in, not from their submitted lab.
In the second half of the course, we will spend our class time on activities to support the final project. This includes project pitches, design reviews, and presentations. The last day of class will be a Demo Day for students to present their final projects.
Late Submissions
In engineering practice, you are regularly required to deliver your work on a specific schedule to meet certain commitments, whether for other internal members of your team or external clients. Deadlines in this class are designed to simulate that reality as you continue to grow as engineers.
We have also broken down larger projects into smaller chunks with intermediate deadlines in order to help you stay on track and give us regular visibility into how you’re progressing. This gives us opportunities to be able to help you through any challenges you might be experiencing before you get too far behind.
With that said, we realize there are many legitimate reasons that you might have a conflict with a particular deadline. If for some reason you are unable to attend class due to illness, sports, or clinic travel, please let us know as soon as possible so that we can discuss an arrangement to help support your learning. In the absence of alternative arrangements, the following policies apply:
| Item | Policy | Why |
|---|---|---|
| Quiz prep | No late work. | TBD |
| Quizzes | No late quizzes. | TBD |
| Labs and check-ins | One late submission accepted, up to one week late. Reach out to an instructor to reschedule. | TBD |
Grading
| Component | Weight |
|---|---|
| Pre-class assessments | 10% |
| Lab check-ins | 30% |
| Quizzes | 15% |
| Final Project | 45% |
Course Kit & Materials
In the first half of the course, we will provide access to custom AI chatbots through Glassroom AI for various components of the course.
The AI tools and policies for the second half of the course, including the final project, are still being defined. More details will be provided.
Collaboration Policy
The collaboration policy varies for different parts of the course:
| Component | Policy | Why |
|---|---|---|
| Quiz prep | Individual; no collaboration. | TBD |
| Labs | Individual; collaboration encouraged. | Submitted work should reflect your own understanding of the material. The lab component of the grade comes primarily from the 1-on-1 check-in, not from the submitted lab. |
| Lab check-ins | Individual; no collaboration. | TBD |
| Quizzes | Individual; no collaboration. | TBD |
| Final project | Individual; collaboration encouraged. | Each student builds their own project, and design reviews and pods give you feedback and support along the way. |
AI Policy
The AI policy varies for different parts of the course:
| Component | Policy | Why |
|---|---|---|
| Pre-class assessments | Use Glassroom | The chatbot we supply is built to prepare you for the quiz. |
| Labs | Use Glassroom | TBD |
| Lab check-ins | No AI allowed | Check-ins are designed to test your understanding of the course content without the assistance of AI. |
| Quizzes | No AI allowed | Quizzes are designed to test your understanding of the course content without the assistance of AI. |
| Final project | TBD | More details will be provided. |
| Writing | No restrictions | Learning to write well alongside these tools is part of learning to practice engineering with them, and what we assess is the quality of the communication itself. |
If you have a question about appropriate AI usage, please don’t hesitate to ask.
Class Culture
The culture of the classroom is very important to us. We are all working to understand these tools as they are rapidly changing. We want to build a culture of curiosity, where asking questions is the norm, and where we thoughtfully explore these tools together. Culture requires that each person contributes, so we hope that you will help us build this kind of learning environment.
The policies below articulate a set of principles that are designed to support that culture. If you have any questions about these or feel that there are others that should be added, please chat with us about it.
In-class Technology Policy
Attention is a precondition for learning. We all deal with many different distractions, but we ask that you do your best while in class to stay focused on what we are doing together. We will be on our computers quite a bit in class as we work on the in-class activities, but we ask that while in class you put on do-not-disturb mode and close out messaging apps, email, or any browser tabs for other work. This is important not only for your attention, but for the attention of your classmates.
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.” We will do our best to put you in a position to be your best selves, and ask that you do your best to do your work with integrity.
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 an 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.