Optimization for Learning and Control Comprehensive resource
providing a masters’ level introduction to optimization theory
and algorithms for learning and control Optimization for Learning
and Control describes how optimization is used in these domains,
giving a thorough introduction to both unsupervised learning,
supervised learning, and reinforcement learning, with an emphasis
on optimization methods for large-scale learning and control
problems. Several applications areas are also discussed, including
signal processing, system identification, optimal control, and
machine learning. Today, most of the material on the optimization
aspects of deep learning that is accessible for students at a
Masters’ level is focused on surface-level computer programming;
deeper knowledge about the optimization methods and the trade-offs
that are behind these methods is not provided. The objective of
this book is to make this scattered knowledge, currently mainly
available in publications in academic journals, accessible for
Masters’ students in a coherent way. The focus is on basic
algorithmic principles and trade-offs. Optimization for Learning
and Control covers sample topics such as: Optimization theory and
optimization methods, covering classes of optimization problems
like least squares problems, quadratic problems, conic optimization
problems and rank optimization. First-order methods, second-order
methods, variable metric methods, and methods for nonlinear least
squares problems. Stochastic optimization methods, augmented
Lagrangian methods, interior-point methods, and conic optimization
methods. Dynamic programming for solving optimal control problems
and its generalization to reinforcement learning. How optimization
theory is used to develop theory and tools of statistics and
learning, e.g., the maximum likelihood method, expectation
maximization, k-means clustering, and support vector machines. How
calculus of variations is used in optimal control and for deriving
the family of exponential distributions. Optimization for Learning
and Control is an ideal resource on the subject for scientists and
engineers learning about which optimization methods are useful for
learning and control problems; the text will also appeal to
industry professionals using machine learning for different
practical applications.
General
Imprint: |
John Wiley & Sons
|
Country of origin: |
United States |
Release date: |
June 2023 |
First published: |
2023 |
Authors: |
Anders Hansson
• Martin Andersen
|
Format: |
Hardcover
|
Pages: |
432 |
ISBN-13: |
978-1-119-80913-5 |
Categories: |
Books >
Computing & IT >
General
|
LSN: |
1-119-80913-4 |
Barcode: |
9781119809135 |
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