Algorithmic Mathematics in Machine Learning

Bastian Bohn, Jochen Garcke, Michael Griebel

ISBN 10: 1611977878 / ISBN 13: 9781611977875

Published by Society for Industrial and Applied Mathematics,U.S., US, 2024

About this Item

The goal of Algorithmic Mathematics in Machine Learning is to explore several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. In this unique book, the authors:Present machine learning methods, review the underlying mathematics, and provide programming exercises intended to deepen the reader's understanding.Accompany application areas with exercises that explore the unique characteristics of real-world data sets (e.g. pedestrian detection image data, biological cell data).Highlight new terminology and background information on mathematical concepts, as well as exercises, in "info-boxes" throughout the text.

Seller Inventory # LU-9781611977875

Bibliographic Details

• Title: Algorithmic Mathematics in Machine Learning
• Publisher: Society for Industrial and Applied Mathematics,U.S., US
• Publication Date: 2024
• Language: English
• ISBN 10: 1611977878
• ISBN 13: 9781611977875
• Binding: Paperback
• Condition: New

About this title

Synopsis

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. 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.

About the Authors

Bastian Bohn is an Akademischer Rat at the Institute for Numerical Simulation, University of Bonn, Germany, where he was previously a postdoctoral researcher. His research interests include machine learning, the mathematics of data science, numerical algorithms in high dimensions, and approximation theory.

Jochen Garcke is a professor of numerics at the Institute for Numerical Simulation, University of Bonn, Germany, and department head at Fraunhofer SCAI (Institute for Algorithms and Scientific Computing), Sankt Augustin, Germany. His research interests include machine learning, scientific computing, reinforcement learning, and high-dimensional approximation.

Michael Griebel is a professor at the Institute for Numerical Simulation, University of Bonn, Germany, where he holds the Chair of Scientific Computing and Numerical Simulation. He is also director of Fraunhofer SCAI (Institute for Algorithms and Scientific Computing), Sankt Augustin, Germany. His research interests include numerical simulation, scientific computing, machine learning, and high-dimensional approximation.