Principles of Machine Learning

Description from DukeHub

Automating prediction and decision-making based on data and past experience. Students will learn how and when to apply supervised, unsupervised, and reinforcement learning techniques, and how to evaluate performance. Common pitfalls such as overfitting and data leakage will be explored and how they can be avoided. Topics include model flexibility and regularization; common supervised learning models and ensembles; performance evaluation techniques; dimensionality reduction; clustering; and the fundamentals of reinforcement learning. Open only to Interdisciplinary Data Science students.

Skills

• ai and machine learningtitle: "machine learning"
• data and statisticsAI review: "how to evaluate performance"

AI-resilient: 2 of 5 2/5

• No: Physical, lab or field work
• ✓Yes: Live work with people · "ensembles"
• ✓Yes: Judgment someone is accountable for · "decision"
• 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 IDS

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• Data Science and Decision Optimization in Banking & Financial Services IDS 586
• Credit Risk Modeling IDS 598-01

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