PHYSICS 123CN
Science of and with Neural Networks
Berndt Mueller
Description from DukeHub
An introductory undergraduate course surveying the building blocks of artificial networks, their basic structure and learning algorithms, from the perceptron to modern deep learning networks and exploring the applications of neural networks in a variety of scientific disciplines, their impact in how we acquire and apply scientific insights, and ethical/philosophical questions that are raised by their use. The course will be primarily structured in modules that feature an overview by the instructor and moderated student presentations.
Skills
- ai and machine learningdescription: "deep learning" and 2 more
- public speakingAI review: "moderated student presentations"
AI-resilient: 1 of 5
- No: Physical, lab or field work
- No: Live work with people
- Yes: Judgment someone is accountable for · "ethical"
- No: A craft you practice with your hands or body
- No: Small, in-person format
The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works
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