Machine learning for physics and astronomy (Record no. 567573)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 01928 a2200157 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780691206417 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 530.0285 |
| Item number | Ac75m |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Acquaviva, Viviana |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning for physics and astronomy |
| Statement of responsibility, etc | Viviana Acquaviva |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Princeton University Press |
| Year of publication | 2023 |
| Place of publication | Princeton |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xvi, 259p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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.<br/>• 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<br/>• Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts<br/>• Includes a wealth of review questions and quizzes<br/>• Ideal for advanced undergraduate and early graduate students in STEM disciplines such as physics, computer science, engineering, and applied mathematics<br/>• Accessible to self-learners with a basic knowledge of linear algebra and calculus<br/>• Slides and assessment questions (available only to instructors<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Physics and astronomy |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| On Display | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 21/07/2025 | 2 | 2897.44 | 530.0285 Ac75m | A186921 | 3863.25 | Books |