Algorithmic Mathematics in Machine Learning
Par šo e-grāmatu
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.
Par autoru
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.
