Algorithmic mathematics in machine learning

By:

Bohn, Bastian

Contributor(s):

Series: Data Science Book SeriesPublication details: Philadelphia: Society for Industrial and Applied Mathematics (SIAM), 2024.Description: xi, 225p.: col. ill.; pbk.: 26 cmISBN:

9781611977875

Subject(s):

DDC classification:

006.31 BOH

Summary: This unique book explores several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. The authors present machine learning methods, review the underlying mathematics, and provide programming exercises to deepen the reader's understanding; accompany application areas with exercises that explore the unique characteristics of real-world data sets (e.g., image data for pedestrian detection, biological cell data); and provide new terminology and background information on mathematical concepts, as well as exercises, in “info-boxes” throughout the text. Audience Algorithmic Mathematics in Machine Learning is intended for mathematicians, computer scientists, and practitioners who have a basic mathematical background in analysis and linear algebra, but little or no knowledge of machine learning and related algorithms. Researchers in the natural sciences and engineers interested in acquiring the mathematics needed to apply the most popular machine learning algorithms will also find this book useful. This book is appropriate for a practical lab or basic lecture course on machine learning within a mathematics curriculum. https://epubs.siam.org/doi/10.1137/1.9781611977882

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	006.31 BOH (Browse shelf(Opens below))	1	Checked out	23/01/2026	035940

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Previous	006.31 ALP Machine learning	006.31 ANA Why machines learn: the elegant maths behind modern AI	006.31 BIS Deep learning: foundations and concepts	006.31 BOH Algorithmic mathematics in machine learning	006.31 BUC From deep learning to rational machines: what the history of philosophy can teach us about the future of Artificial Intelligence	006.31 BUD Fundamentals of deep learning: designing next generation machine intelligence algorithms	006.31 BUR Hundred-page machine learning book	Next

Includes bibliographical references and index

This unique book explores several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. The authors present machine learning methods, review the underlying mathematics, and provide programming exercises to deepen the reader's understanding;

accompany application areas with exercises that explore the unique characteristics of real-world data sets (e.g., image data for pedestrian detection, biological cell data); and

provide new terminology and background information on mathematical concepts, as well as exercises, in “info-boxes” throughout the text.

Audience

Algorithmic Mathematics in Machine Learning is intended for mathematicians, computer scientists, and practitioners who have a basic mathematical background in analysis and linear algebra, but little or no knowledge of machine learning and related algorithms. Researchers in the natural sciences and engineers interested in acquiring the mathematics needed to apply the most popular machine learning algorithms will also find this book useful.

This book is appropriate for a practical lab or basic lecture course on machine learning within a mathematics curriculum.

https://epubs.siam.org/doi/10.1137/1.9781611977882

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