This book systematically classifies the mathematical formalisms of computational models that are required for solving problems in mathematics, engineering and various other disciplines. It also provides numerical methods for solving these problems using suitable algorithms and for writing computer codes to find solutions. For discrete...

Arising from courses taught by the authors, this largely self-contained treatment is ideal for mathematicians who are interested in applications or for students from applied fields who want to understand the mathematics behind their subject. Early chapters cover Fourier analysis, functional analysis, probability and linear algebra, all of...

This book is aimed at relatively technical readers, though no prior experience with
Continuous Integration is assumed. You may be new to Continuous Integration, and
would like to learn about the benefits it can bring to your development team. Or, you
might be using Jenkins or Hudson already, and want to discover how you can...

This informally written text provides students with a clear introduction into the subject of linear algebra. Topics covered include matrix multiplication, row reduction, matrix inverse, orthogonality and computation. The self-teaching book is loaded with examples and graphics and provides a wide array of probing problems, accompanying...

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most...

This book is dedicated to the application of the different theoretical models described in Volume 1 to identify the near-, mid- and far-infrared spectra of linear and nonlinear triatomic molecules in gaseous phase or subjected to environmental constraints, useful for the study of environmental sciences, planetology and...

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action,...

Are you familiar with the IEEE floating point arithmetic standard? Would you like to understand it better? This book gives a broad overview of numerical computing, in a historical context, with a special focus on the IEEE standard for binary floating point arithmetic. Key ideas are developed step by step, taking the reader from floating point...

Analysis and Control of Boolean Networks presents a systematic new approach to the investigation of Boolean control networks. The fundamental tool in this approach is a novel matrix product called the semi-tensor product (STP). Using the STP, a logical function can be expressed as a conventional discrete-time linear system. In the light of...

The rapidly growing amount of data, available from different technologies in the field of bio-sciences, high-energy physics, economy, climate analysis, and in several other scientific disciplines, requires a new generation of machine learning and statistical methods to deal with their complexity and heterogeneity. As data collections becomes...

The focus of the book, Development of Aneurysms, is a detailed discussion of the biology of aneurysms. Aneurysm formation is influenced by alterations in arterial wall protein synthesis, hemodynamic forces, arterial wall inflammation, and matrix protein degradation. The relationships of these forces provide a unified theory for the...

There are some books that target the theory of the finite element, while others focus on the programming side of things. Introduction to Finite Element Analysis Using MATLAB® and Abaqus accomplishes both. This book teaches the first principles of the finite element method. It presents the theory of the finite...