Machine learning for the physical sciences : fundamentals and prototyping with Julia
Material type:
TextPublication details: Boca Raton, FL : CRC Press/Taylor & Francis Group, 2024. Description: xxi, 266 pages : illustrations (90 B/W Illustrations) ; 25 cmISBN: 9781032395234Subject(s): Physical sciences -- Textbooks -- Data processing | Machine learning -- Textbooks | Julia (Computer program language) -- TextbooksDDC classification: 53:004.85 Online resources: Table of content | Reviews Summary: Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.
| Item type | Current library | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
Book
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NISER LIBRARY | 53:004.85 CUN-M (Browse shelf(Opens below)) | Available | 25783 |
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| 53:004.42 CHA-C Computer applications in physics with Fortran, Basic and C | 53:004.8 KNE-A AI for physics | 53:004.85 BOL-M Machine learning : a physicist perspective | 53:004.85 CUN-M Machine learning for the physical sciences : fundamentals and prototyping with Julia | 530(076.5) SQU-P Practical physics | 530(076.5) SQU-P Practical physics | 530(076.5) SQU-P Practical physics |
Includes bibliographical references (pages 247-259) and index.
Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.
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