Machine learning for physics and astronomy / (Record no. 87259)

MARC details
000 -LEADER
fixed length control field 02284cam a2200289 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251106105238.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230106s2023 njua ob 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780691206417
040 ## - CATALOGING SOURCE
Transcribing agency STCPL
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number VIV-M
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Acquaviva, Viviana
245 10 - TITLE STATEMENT
Title Machine learning for physics and astronomy /
Statement of responsibility, etc. by Viviana Acquaviva
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Princeton :
Name of publisher, distributor, etc. Princeton University Press ,
Date of publication, distribution, etc. 2023.
300 ## - PHYSICAL DESCRIPTION
Extent 259 p.
Other physical details Paper Back
520 ## - SUMMARY, ETC.
Summary, etc. "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 conceptsIncludes a wealth of review questions and quizzesIdeal 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 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Physics
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Astrophysics
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Astronomy
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Physics
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data Science
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Collection code Not for loan Home library Current library Shelving location Date acquired Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification   Physics Reference Book ST. THOMAS COLLEGE LIBRARY, PALAI ST. THOMAS COLLEGE LIBRARY, PALAI Physics - Reference 2025-11-06 4005.00   006.31 VIV-M 93158 2025-11-06 2025-11-06 Books
Rights reserved ©2021 ST. THOMAS COLLEGE LIBRARY
A joint venture of - St. Thomas College Library and
Department of Computer Science, St. Thomas Collge Palai