Quantum computing explained in terms of elementary linear algebra, emphasizing computation and algorithms and requiring no background in physics. This …
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides …
Has the abstract nature of linear algebra ever left you overwhelmed? Do you yearn to unlock the essence of machine …
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
As the name implies, Intermediate Dynamics: A Linear Algebraic Approach views "intermediate dynamics"--Newtonian 3-D rigid body dynamics and analytical mechanics--from …
Designed to help people solve numerical problems on small computers, this book's main subject areas are numerical linear algebra, function …
A collection of selected refereed papers presented at the International Conference on Statistics, Combinatorics and Related Areas, and the Eighth …
Matrix Analysis and Applied Linear Algebra is an honest math text that circumvents the traditional definition-theorem-proof format that has bored …
This book avoids the traditional definition-theorem-proof format; instead a fresh approach introduces a variety of problems and examples all in …
Skew spectrum of the Cartesian product of an oriented graph with an oriented hypercube / A. Anuradha, R. Balakrishnan -- …
This volume contains a collection of clever mathematical applications of linear algebra, mainly in combinatorics, geometry, and algorithms. Each chapter …
Lectures: T.H. Brylawski: The Tutte polynomial.- D.J.A. Welsh: Matroids and combinatorial optimisation.- Seminars: M. Barnabei, A. Brini, G.-C. Rota: Un’introduzione …
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. The first …
This book expands the lectures given at IWOTA 05 (International Workshop on Operator Theory and Applications) which was held at …
This introduction to quantum algorithms is concise but comprehensive, covering many key algorithms. It is mathematically rigorous but requires minimal …
An integral part of this text, the Study Guide incorporates detailed solutions to every third odd-numbered exercise, as well as …
This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this …
Graphs are among the most important abstract data types in computer science, and the algorithms that operate on them are …
Mathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, …
