This text provides a very simple, initial introduction to the complete scientific computing pipeline: models, discretization, algorithms, programming, verification, and visualization. The book is easy to read and only requires a command of one-variable calculus and some very basic knowledge about computer programming.
These books are at the first-year graduate level or a little higher, depending on one’s university. The list includes basic & advanced algrebra and basic & advanced real analysis.
Since the research on text algorithms continues, it is not possible to have a book that completely covers the area. Looking at the table of contents of this book, its fifteen chapters cover nicely many of the major developments in the field. Crochemore and Rytter have succeeded in producing a textbook that is as thorough as it is timely.
Written by two of the leading researchers in the area, this engaging and accessible book is unique in covering the diverse foundations of multiagent systems, including logic. Its extensive treatment of the interplay between computer science and game theory will define how the subject should be taught.
This book emphasizes two important areas. First, it reviews the framework within which computer science and the study of algorithms and data structures must fit, in particular, the reasons why we need to study these topics and how understanding these topics helps us to become better problem solvers. Second, we review the Python programming language.
Calculus Made Easy is a book where students will learn and grasp the true essence of calculus without any added fluff or overt technicality. Most college calculus texts weigh a ton; this one does not – it just gets to the point.
This book is meant to provide an introduction to vectors, matrices, and least squares methods, basic topics in applied linear algebra. Our goal is to give the beginning student, with little or no prior exposure to linear algebra, a good grounding in the basic ideas, as well as an appreciation for how they are used in many applications.
Mining of Massive Datasets focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. This book also takes an algorithmic point of view: data mining is about applying algorithms to data, rather than using data to ‘train’ a machine-learning engine of some sort.
Why is this book different from all other books on mathematical probability and statistics? The key aspect is the book’s consistently applied approach, especially important for engineering students.