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Machine Learning for Physics and Astronomy

Name: Machine Learning for Physics and Astronomy
Brand: Princeton University Press
SKU: 9780691206417
Price: 47.99 USD
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Contributors:

Viviana Acquaviva

Publisher:

Princeton University Press

Binding:

Paperback

Book Category:

Science

Book Subcategory:

Physics, Space Science, Research & Methodology

Book Topic:

Mathematical & Computational, Astronomy

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The product is eligible for a complimentary return within 30 days from the date of purchase. Furthermore, individual items are shipped within 24 hours of receiving the order.

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Specifications +

Release Date:

2023-08-15

ISBN 13:

9780691206417

Weight:

1.45 lbs

Size:

9.92 x 7.95 x 0.87 inches

Pages:

280

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product description

Description

Machine Learning for Physics and Astronomy

A hands-on introduction to machine learning and its applications to the physical sciences

As the size and complexity of data continue to grow exponentially across the physical sciences, machine learning is helping scientists to sift through and analyze this information while driving breathtaking advances in quantum physics, astronomy, cosmology, and beyond. This incisive textbook covers the basics of building, diagnosing, optimizing, and deploying machine learning methods to solve research problems in physics and astronomy, with an emphasis on critical thinking and the scientific method.

Using a hands-on approach to learning, Machine Learning for Physics and Astronomy draws on real-world, publicly available data as well as examples taken directly from the frontiers of research, from identifying galaxy morphology from images to identifying the signature of standard model particles in simulations at the Large Hadron Collider.

Key Features

Introduces readers to best practices in data-driven problem-solving, from preliminary data exploration and cleaning to selecting the best method for a given task
Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts
Includes a wealth of review questions and quizzes
Ideal for advanced undergraduate and early graduate students in STEM disciplines such as physics, computer science, engineering, and applied mathematics
Accessible to self-learners with a basic knowledge of linear algebra and calculus
Slides and assessment questions (available only to instructors)

Real-World Applications

This textbook bridges theory and practice by incorporating authentic research scenarios. Students work with publicly available datasets and tackle problems directly from cutting-edge research, including galaxy morphology classification and particle physics simulations from the Large Hadron Collider. The Python-based Jupyter Notebook format provides an interactive learning environment that reinforces concepts through practical application.

About the Author

Viviana Acquaviva is professor of physics at the New York City College of Technology and the Graduate Center, City University of New York, and the recipient of a PIVOT fellowship to apply AI tools to problems in climate. She was named one of Italy's fifty most inspiring women in technology by InspiringFifty, which recognizes women in STEM who serve as role models for girls around the world.

Machine Learning for Physics and Astronomy

Description

Machine Learning for Physics and Astronomy

Key Features

Real-World Applications

About the Author

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Machine Learning for Physics and Astronomy

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Description

Machine Learning for Physics and Astronomy

Key Features

Real-World Applications

About the Author

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