A diverse selection of data science topics explored through a mathematical lens.

This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Mathematical science communication, as well as the field of science communication in general, has gained momentum over the last few decades. Mathematical science communication aims to inform the public about contemporary research, enhance factual and methodological knowledge, and foster a greater interest and support for the science of mathematics. This enables the public to apply it to their practical life, and to decision-making on a greater scale. These objectives are met in the various formats and media through which mathematical science communication is brought to the public.The first 13 chapters of the book consist of best-practice examples from the areas of informal math education, museums and exhibitions, and the arts. The final 5 chapters discuss the structural aspects of mathematical science communication and contribute to the basis for its theoretical framework.

This book is for anyone who wishes to illustrate their mathematical ideas, which in our experience means everyone. It is organized by material, rather than by subject area, and purposefully emphasizes the process of creating things, including discussions of failures that occurred along the way. As a result, the reader can learn from the experiences of those who came before, and will be inspired to create their own illustrations. Topics illustrated within include prime numbers, fractals, the Klein bottle, Borromean rings, tilings, space-filling curves, knot theory, billiards, complex dynamics, algebraic surfaces, groups and prime ideals, the Riemann zeta function, quadratic fields, hyperbolic space, and hyperbolic 3-manifolds. Everyone who opens this book should find a type of mathematics with which they identify. Each contributor explains the mathematics behind their illustration at an accessible level, so that all readers can appreciate the beauty of both the object itself and the mathematics behind it.

New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.