Engineering Artificial Intelligence
AI as an engineering design tool and an element of intelligent systems
EAI
An introduction to artificial/machine intelligence. The study of this evolving and diverse topic begins with a survey and classification of techniques, including search-based, logic-based, statistical, and embodied. Applications of intelligent technologies explored include natural language processing, vision, expert knowledge, game-playing, and several robotics applications. Upon conclusion of the survey, the focus of the course turns to a special topic chosen by the instructor. The instructor could choose, for instance, machine learning, embodiment, evolutionary robotics, or artificial life—or a project.
Synchronous Zoom is available (see button below), but in-person attendance is strongly encouraged.
Log in to Join ZoomEveryone is required to join the messaging service called "Discord." We'll use it to communicate with each other during the semester. The Discord server you need to join is called drico. That's an invitation link.
Be sure to join the channel #467-567.
The primary and only required textbook is Russell and Norvig, 2020.
My lecture notes with fill-in-the-blanks are available at the following link:
Students enrolled in MME 567 are required to complete an applied project with a written report in addition to the regular coursework. The project involves applying one AI technique from the course to a well-defined engineering problem.
Select one AI technique covered in the course and apply it to one engineering or robotics problem. You should use existing frameworks (e.g., PyTorch, scikit-learn, OpenAI Gymnasium) and existing datasets or simulation environments. The focus is on understanding, application, and analysis—not building everything from scratch.
The following are examples of appropriate project scope:
| Technique | Example Problem | Tools/Data |
|---|---|---|
| A* / heuristic search | Factory robot path planning with obstacles | Custom grid world |
| Supervised learning | Predictive maintenance from bearing vibration data | scikit-learn + CWRU bearing dataset |
| CNN | Visual defect detection in manufactured parts | PyTorch + casting or steel surface defect dataset |
| RNN or Transformer | Remaining useful life prediction from engine sensor time series | PyTorch + NASA C-MAPSS turbofan dataset |
| RL (Q-learning or deep RL) | Robotic control task (e.g., CartPole, LunarLander, robotic arm) | OpenAI Gymnasium + Stable Baselines3 |
You are not limited to these examples. You may propose any project that applies a technique from the course to an engineering problem, subject to instructor approval.
IEEE two-column conference format (templates here), 5–10 pages, with the following sections:
All deliverables are submitted via a Git repository link on Moodle. Your repository should include a README explaining how to run your code.
| Milestone | Deliverable | Description |
|---|---|---|
| Week 14 | Proposal | Problem statement, chosen technique, dataset or environment, and project plan (1 page) |
| Week 15 | Progress check-in | Working code with preliminary results or proof-of-concept; note any roadblocks |
| Week 16 | Final report + code | Complete report and working code in your repository |
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Any use of technology that misleads a reviewer in assessing the student's mastery of a specific set of skills or knowledge is a type of intellectual dishonesty, that is, a type of cheating. Students who are unsure about the appropriateness of using an artificial intelligence tool (or "AI") must check with the instructor before using it. This includes the use of tools that generate text, images, video, code, and other works. If you are permitted by your instructor to use one or more AI tools in producing your work, you must disclose the use of that tool. You should say which tool you used and how you used it. Then if you use specific AI generated content (text, images, videos, audio, code, and so on) you must cite it in the style (APA, MLA, and so on) specified by your instructor.
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