AIPI 540
Deep Learning Applications
Brinnae Marie Bent
Description from DukeHub
This course focuses on the primary use cases of deep learning to solve challenging problems particularly in the areas of computer vision, natural language processing, and recommendation systems. Students will develop an understanding of both the mathematical theory and programmatic implementation of neural networks using Python. The course includes significant hands-on team-based project work and an emphasis on self-discovery. The semester will be split into three core modules: computer vision, NLP, and recommendation systems. Each module will include lectures, assignments, a team project, and review of recent research in the field.
Skills
- programmingdepartment: AIPI
- ai and machine learningdescription: "deep learning" and 3 more
AI-resilient: 2 of 5
- Yes: Physical, lab or field work · "hands-on"
- Yes: Live work with people · "team"
- No: Judgment someone is accountable for
- 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
More in AIPI
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- Mid-Program Residency AIPI 505
- Optimization in Practice AIPI 530
- Capstone Practicum 1 AIPI 549
- Reinforcement Learning AIPI 590-01
- Applied Computer Vision AIPI 590-03