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.

About the Author

Bastian Bohn is an Akademischer Rat at the Institute for Numerical Simulation, University of Bonn, Germany, where he was previously a Research Scientist. 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 University of Bonn and Department Head at the 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 University of Bonn, Germany, where he also holds the Chair of Scientific Computing and Numerical Simulation. He is also Director of Fraunhofer SCAI, Schloss Birlinghoven, German. His research interests include numerical simulation, scientific computing, machine learning, and high-dimensional approximation.

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