Machine Learning for Physics and Astronomy by Viviana Acquaviva (.PDF)

File Size: 61.0 MB

Machine Learning for Physics and Astronomy by Viviana Acquaviva
Requirements: .PDF reader, 61.0 MB
Overview: 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. What Is Machine Learning? To the best of my knowledge/ability to explain, I would say that it’s the process of teaching a machine to make informed, data-driven decisions. Examples of such decisions include recognizing and characterizing objects based on similarities or differences, detecting patterns, and distinguishing signal from noise. Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts.
Genre: Non-Fiction > Tech & Devices

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