Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic...

This easy to read textbook provides an introduction to computer architecture, while focusing on the essential aspects of hardware that programmers need to know. The topics are explained from a programmer’s point of view, and the text emphasizes consequences for programmers. Divided in five parts, the book covers the basics of digital...

Combining knowledge with strategies, Data Structure Practice for Collegiate Programming Contests and Education presents the first comprehensive book on data structure in programming contests. This book is designed for training collegiate programming contest teams in the nuances of data structure and for helping college students in...

Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of...

Presenting cutting-edge research, Intrusion Detection in Wireless Ad-Hoc Networks explores the security aspects of the basic categories of wireless ad-hoc networks and related application areas. Focusing on intrusion detection systems (IDSs), it explains how to establish security solutions for the range of wireless networks, including...

Combining academic and practical approaches to this important topic, Numerical and Analytical Methods with MATLAB^® for Electrical Engineers is the ideal resource for electrical and computer engineering students. Based on a previous edition that was geared toward mechanical engineering students, this book expands many of...

Traditional books on machine learning can be divided into two groups ― those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning...