000			01928 a2200157 4500
020			_a9780691206417
082			_a530.0285 _bAc75m
100			_aAcquaviva, Viviana
245			_aMachine learning for physics and astronomy _cViviana Acquaviva
260			_bPrinceton University Press _c2023 _aPrinceton
300			_axvi, 259p
520			_aAs 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
650			_aPhysics and astronomy
650			_aMachine learning
942			_cBK
999			_c567573 _d567573