Reference library

This page provides a reading map for the engineering-AI curriculum. The maintained, machine-readable bibliography and access notes are in the repository reference library.

Start with your purpose

Purpose Recommended sources What they should change in the curriculum
Teach ML foundations to engineers Simeone; McClarren; Naser Explain a method through a physical question, baseline, and held-out evaluation.
Design a shared cross-disciplinary course Singelmann and Covarrubias; application-studio framework Keep foundational ML shared; adapt cases rather than duplicate introductory courses by department.
Build AI literacy and assessment Long and Magerko; UNESCO; NIST AI RMF; engineering GenAI reviews Teach task framing, provenance, verification, responsibility, and individual evidence of understanding.
Build a project or capstone Challenge/project-based-learning reviews; robotics literature Require staged evidence, a testable artifact, failure analysis, and reflection.
Add physics and dynamics Brunton and Kutz; PINN and thermal-fluid reviews Compare data-driven models with physical, numerical, or control baselines.
Teach agentic engineering MCP specification; skills documentation Teach specifications, permissions, tool schemas, logging, verification, and human responsibility.
Publish reusable written material The Carpentries; Jupyter Book Use explicit outcomes, formative checks, accessibility, and reproducible public artifacts.

Reading paths

Access and reuse boundary

The presence of a citation or library copy does not grant redistribution rights. The public repository should publish original explanations, original diagrams, rights-cleared data cards, short code, and stable links. It should not host publisher PDFs, restricted e-books, confidential material, or unreviewed student data.

Use the repository access and license notes before reusing a figure, table, substantial excerpt, code fragment, or dataset.