The Mathematics of Machine Learning: Lectures on Supervised Methods and Beyond -- Maria Han Veiga, Paperback

This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics.

There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction.

This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.

Author: Maria Han Veiga, Fran輟is Gaston Ged
Publisher: de Gruyter
Published: 05/20/2024
Pages: 210
Binding Type: Paperback
Weight: 0.75lbs
Size: 9.61h x 6.69w x 0.44d
ISBN: 9783111288475

About the Author

Dr. Maria Han Veiga,
Assistant professor of mathematics, Ohio State University, Ohio, USA
Prior to joining Ohio State, she was a postdoctoral fellow at the University of Michigan in Mathematics and Data Science (MIDAS). She obtained her PhD at the University of Zurich. Her research focuses on numerical analysis for hyperbolic partial differential equations and scientific machine learning.

Dr. Fran輟is Ged
Postdoctoral fellow, University of Vienna, Austria
He obtained his PhD in Mathematics at the University of Zurich, Switzerland, after which he was a postdoc fellow at the ﾉcole Polytechnique F馘駻ale de Lausanne. His research interests gravitate around the theory of deep learning and reinforcement learning, as well as mathematical population genetics and growth-fragmentation processes.