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Total Size:
59.3 MB
Info Hash:
F692E45463C949298BBE5A781C0C51C8F4B66873
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Aug. 26, 2025, 10:55 a.m.
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(Last updated: Sept. 20, 2025, 1:41 a.m.)
| File | Size |
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| Calder J. Linear Algebra, Data Science, and Machine Learning 2025.pdf | 59.3 MB |
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40.8 MB
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| Uploaded by freecoursewb | Size 40.8 MB | Health [ 21 /14 ] | Added 2023-10-29 |
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NOTE
SOURCE: Calder J. Linear Algebra, Data Science, and Machine Learning 2025
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COVER
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MEDIAINFO
Textbook in PDF format This text provides a mathematically rigorous introduction to modern methods of Machine Learning (ML) and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary Machine Learning and data analysis. The book will provide a firm foundation to the reader whose goal is to work on applications of Machine Learning and/or research into the further development of this highly active field of contemporary applied mathematics. To introduce the reader to a broad range of Machine Learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python notebooks complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject. The Students’ Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors’ Solutions Manual from the link supplied on the text’s Springer website. For the computational activities associated with this text, access to a reasonably powerful computer (a decent laptop will suffice) and the internet is assumed. We rely on the increasingly popular open source programming language Python. Any student who has some computer programming experience can easily ramp up to speed in Python by working through the notebooks listed below. Additional Python notebooks appear throughout the text, and are all publicly available on a GitHub website. One reason Python has become widely used in a variety of applications is the availability of high quality third party Python packages for tasks such as numerical analysis, data analysis, scientific computation, deep learning, etc. We will make extensive use of several packages in this text, including numpy, scipy, sklearn, pandas, pytorch, and graphlearning, the last of which was created by the first author. Many of these packages are introduced by the way of examples in the accompanying notebooks. We will assume the reader is eventually able to achieve familiarity with the numpy and pandas packages, via the Python notebooks listed below. In addition, there is an introduction to pytorch in a notebook at the start of Chapter 10. The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in Data Science and Machine Learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, Computer Science, engineering, biology, economics and finance, and so on
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