Students and readers
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Review background concepts, navigate chapter guides, and explore the catalog of Application Examples.
Open learning resourcesCompanion portal ยท Yan Jin
Machine Learning for Engineering Applications
A companion portal connecting mathematical foundations, modern learning methods, and engineering judgment through practical examples.
Start where you are
One book, three ways in
Move directly into the materials and conversations most useful to you.
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Teach with context
Use course pathways, chapter notes, assessment guidance, and protected instructor materials.
View instructor accessEngineering community
Extend the field guide
Discuss engineering choices, report errata, and contribute insights, examples, and resources.
Join the community15 chapters
From foundations to engineering systems
The book develops essential mathematics first, then connects learning paradigms to responsible engineering decisions.
Mathematical foundations
- Linear algebra
- Probability and statistics
- Optimization
- Machine learning foundations
Concepts and algorithms
- Regression and classification
- Ensemble and deep learning
- Unsupervised and reinforcement learning
- Generative and physics-informed ML
Engineering emphasis
Models are only part of the decision.
Frame the problem. Match methods to data, physics, constraints, and consequences.
Make it executable. Connect concepts to transparent Python implementations.
Build judgment. Evaluate reliability, interpretation, and deployment in context.