Geo.Engineerings

MATLAB: A Practical Introduction to Programming and Problem Solving, Fourth Edition, has been updated to reflect the functionality of the current version of MATLAB, including the new H2 Graphics system. It features new and revised end-of-chapter exercises, more engineering applications to help the reader learn this software tool in context, and a new section on object-oriented programming in MATLAB. MATLAB has become the standard software tool for solving scientific and engineering problems due to its powerful built-in functions and its ability to program. Assuming no knowledge of programming, this book guides the reader both programming and built-in functions to easily exploit MATLAB's extensive capabilities for tackling engineering problems. The book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. In-depth coverage is given to input/output, a topic fundamental to many engineering applications. Presents programming concepts and MATLAB built-in functions side-by-side * Offers a systematic, step-by-step approach, building on concepts throughout the book and facilitating easier learning * Includes sections on common pitfalls and programming guidelines to direct students toward best practices * Combines basic programming concepts, built-in functions, and advanced topics for problem solving with MATLAB to make this book uniquely suitable for a wide range of courses teaching or using MATLAB across the curriculum.

Brian Hahn, Daniel T. Valentine ... 428 pages - Publisher: Academic Press; 6th edition (November, 2016) ... Language: English - ISBN-10: 0081008775 - ISBN-13: 978-0081008775

Essential MATLAB for Engineers and Scientists, Sixth Edition, provides a concise, balanced overview of MATLAB's functionality that facilitates independent learning, with coverage of both the fundamentals and applications. The essentials of MATLAB are illustrated throughout, featuring complete coverage of the software's windows and menus. Program design and algorithm development are presented clearly and intuitively, along with many examples from a wide range of familiar scientific and engineering areas. This updated edition includes the latest MATLAB versions through 2016a, and is an ideal book for a first course on MATLAB, or for an engineering problem-solving course using MATLAB, as well as a self-learning tutorial for professionals and students expected to learn and apply MATLAB. Updated to include all the newer features through MATLAB R2016a. * Includes new chapter on complex variables analysis. * Presents a comparison of execution time between compiled and un-compiled code that includes examples. * Describes the new H2 graphics features.

Justin Solomon ... 400 pages - Publisher: CRC Press; (July, 2015) ...
Language: English - ISBN-10: 1482251884 - ISBN-13: 978-1482251883 ... 

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.

The book covers a wide range of topics―from numerical linear algebra to optimization and differential equations―focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students’ intuition while introducing extensions of the basic material.

The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.

Sergios Theodoridis ... 1062 pages - Publisher: Academic Press; 1st edition (April, 2015) ... Language: English - ISBN-10: 0128015225 - ISBN-13: 978-0128015223 ... 

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.The book builds carefully from the basic classical methods  to  the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for  different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. * The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. * Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. * MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.