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Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control
Discover how optimization powers machine learning, aerospace design, and control theory in this foundational roadmap by Steve Brunton.
This overview lecture introduces the core mathematical structure, applications, and roadmap for a comprehensive optimization bootcamp. Steve Brunton outlines how optimization problems combine an objective function to minimize or maximize subject to constraint equations, highlighting its role as the mathematical engine powering machine learning, control theory, aerospace design, and industrial manufacturing.
Lecture Guide
- 1Optimization is formalized as minimizing or maximizing an objective function subject to constraint equations.
- 2Nearly all modern machine learning models and neural networks are trained using optimization algorithms like stochastic gradient descent.
- 3Control theory problems can be framed as optimization problems constrained by the physical dynamics of a system.
- 4Convex optimization problems guarantee that any local minimum found is also a global minimum.
Proofs, Examples & Demos
Assumed Background
Part of course·Lecture 1 of 9
ME 565: Optimization Bootcamp: Convex Optimization for ML & Control
Start of sequence
Next · Lecture 2Opening lecture of 9-session syllabus
The Anatomy of an Optimization Problem
About the lecturer
Professor of Mechanical Engineering & Adjunct Professor of Applied Mathematics, University of Washington
University of Washington Professor known for data-driven dynamical systems, SINDy, Koopman operator theory, and control engineering.
2 courses · 22 lectures in archiveView profile
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