Machine Learning for Physics and Astronomy

Name: Machine Learning for Physics and Astronomy
Brand: Princeton University Press
SKU: 9780691206417
Price: 46.85 EUR
Availability: InStock

Autor: Viviana Acquaviva

Jazyk:

Väzba: Brožovaná

Vydavateľ: Princeton University Press

Dostupnosť: Skladom u dodávateľa

Odosielame za 9-15 dní

46.85 €

A hands-on introduction to machine learning and its applications to the physical sciencesAs the size...

Informácie o knihe

Autor

Viviana Acquaviva

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2023

Stránok

256

EAN

9780691206417

ISBN

0691206414

Enbook ID

42312409

Vydavateľ

Princeton University Press

Hmotnosť

666

Rozmery

203 x 254 x 15

Kompletný popis

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.

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)

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