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Introduction to Linear Programming with MATLAB
This book is based on the lecture notes of the author delivered to the students at the Institute of Science, Banaras Hindu University, India. It covers simplex, revised simplex, two-phase method, duality, dual simplex, complementary slackness, transportation and assignment problems with good number of examples, clear proofs, MATLAB codes and homework problems. The book will be useful for both students and practitioners.
R 9,324.22
Introduction to 3D Game Programming w DirectX11
This updated bestseller provides an introduction to programming interactive computer graphics, with an emphasis on game development using DirectX 11. The book is divided into three main parts: basic mathematical tools, fundamental tasks in Direct3D, and techniques and special effects. It includes new Direct3D 11 features such as hardware tessellation, the compute shader, dynamic shader linkage and covers advanced rendering techniques such as screen-space ambient occlusion, level-of-detail handling, cascading shadow maps, volume rendering, and character animation. Includes a companion CD-ROM with code and figures.eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at info@merclearning.com.
R 1,277.00
Introduction to C++ and Numerical Methods
In addition to being an introduction to C++, this text also provides clear explanations of the basics of numerical methods, and is unique for its coverage of numerical methods used in scientific and engineering computation. In addition there is a general discussion of some of the basic paradigms for writing good programs and detecting errors. The result is a brief yet comprehensive treatment of the subject.
R 5,591.00
Introduction to Game Programming in C++
Introduction to Game Programming with C++ explores the world of game development with a focus on C++. This book begins with an explanation of the basics of mathematics as it relates to game programming, covers the fundamentals of C++, and describes a number of algorithms commonly used in games. In addition, it discusses several libraries that can help you manage graphics, add audio, and create installation software so you can get started on the path to making both 2D and 3D games.With this book understand the basics of programming in C++, including working with variables, constants, arrays, conditional statements, pointers, and functions; learn how to use the ClanLib library to make 2D games; discover how the OGRE graphics library can be used to implement particle systems and other effects in 3D games; find out how to integrate sound and music into your game.
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R 611.00
Introduction to Sequential Monte Carlo
This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics.The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book.Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed.The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a “Python corner,” which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.
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Practical Introduction to Fuzzy Logic using LISP
This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to “teach” the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications. The complete set of functions that make up the Fuzzy LISP programming toolbox can be downloaded from a companion book’s website.
R 3,873.51
Introduction to Statistical Learning
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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
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Introduction to Object COBOL
Are You Ready to Meet the Demand for Object COBOL Programming? Now you can keep up-to-date with the newest standards in COBOL. With its use of straightforward language and real-world examples, An Introduction To Object COBOL is a concise and accessible introduction to using Object COBOL. You'll discover how object technology is applied and how the resulting Object COBOL code is constructed. All of the programs included in the text can be downloaded from the Wiley web site (www.wiley.com/college/doke). You are encouraged to execute and experiment with them. As you work with these programs and follow the related case study, you will gain important experience in using Object COBOL in a business environment. Inside these pages you will: * Keep up-to-date with the state-of-the-art in COBOL programming. * Understand the relationship of object technology to the business environment. * See how object technology is applied to real-life examples. * Receive a non-technical introduction to object technology without the use of intensive vocabulary.
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Introduction to Concurrency in Programming Languages
Exploring how concurrent programming can be assisted by language-level techniques, Introduction to Concurrency in Programming Languages presents high-level language techniques for dealing with concurrency in a general context. It provides an understanding of programming languages that offer concurrency features as part of the language definition.The book supplies a conceptual framework for different aspects of parallel algorithm design and implementation. It first addresses the limitations of traditional programming techniques and models when dealing with concurrency. The book then explores the current state of the art in concurrent programming and describes high-level language constructs for concurrency. It also discusses the historical evolution of hardware, corresponding high-level techniques that were developed, and the connection to modern systems, such as multicore and manycore processors. The remainder of the text focuses on common high-level programming techniques and their application to a range of algorithms. The authors offer case studies on genetic algorithms, fractal generation, cellular automata, game logic for solving Sudoku puzzles, pipelined algorithms, and more. Illustrating the effect of concurrency on programs written in familiar languages, this text focuses on novel language abstractions that truly bring concurrency into the language and aid analysis and compilation tools in generating efficient, correct programs. It also explains the complexity involved in taking advantage of concurrency with regard to program correctness and performance.
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Introduction to Bayesian Inference, Methods and Computation
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
R 2,405.84
Introduction to Stochastic Programming
The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)
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Introduction to Stochastic Programming
The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)
R 3,127.00
Introduction to Linear Goal Programming
Provides a concise and lucid overview of the linear goal programming model, a computationally efficient algorithm for solution, duality and sensitivity analysis, and extensions of the methodology to integer as well as nonlinear models.
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Introduction to Meshfree Methods and Their Programming
The finite difference method (FDM) hasbeen used tosolve differential equation systems for centuries. The FDM works well for problems of simple geometry and was widely used before the invention of the much more efficient, robust finite element method (FEM). FEM is now widely used in handling problems with complex geometry. Currently, we are using and developing even more powerful numerical techniques aiming to obtain more accurate approximate solutions in a more convenient manner for even more complex systems. The meshfree or meshless method is one such phenomenal development in the past decade, and is the subject of this book. There are many MFree methods proposed so far for different applications. Currently, three monographs on MFree methods have been published. Mesh Free Methods, Moving Beyond the Finite Element Method d by GR Liu (2002) provides a systematic discussion on basic theories, fundamentals for MFree methods, especially on MFree weak-form methods. It provides a comprehensive record of well-known MFree methods and the wide coverage of applications of MFree methods to problems of solids mechanics (solids, beams, plates, shells, etc.) as well as fluid mechanics. The Meshless Local Petrov-Galerkin (MLPG) Method d by Atluri and Shen (2002) provides detailed discussions of the meshfree local Petrov-Galerkin (MLPG) method and itsvariations. Formulations and applications of MLPG are well addressed in their book.
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Introduction to C++
This book is primarily for students who are taking a course on the C++ language, for those who wish to self-study the C++ language, and for programmers who have experience with C and want to advance to C++. It could also prove useful to instructors of the C++ course who are looking for explanatory programming examples to add in their lectures. The focus of this book is to provide a solid introduction to the C++ language and programming knowledge through a large number of practical examples and meaningful advice. It includes more than 500 exercises and examples of progressive difficulty to aid the reader in understanding the C++ principles and to see how concepts can materialize in code.The examples are designed to be short, concrete, and substantial, quickly giving the reader the ability to understand how to apply correctly and efficiently the features of the C++ language and to get a solid programming know-how. Rest assured that if you are able to understand this book’s examples and solve the exercises, you can safely go on to edit larger programs, you will be able to develop your own applications, and you will have certainly established a solid fundamental conceptual and practical background to expand your knowledge and skills.
R 2,161.00
Introduction to Image Classification
Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involves feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural networks. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight.The book starts from introducing methods for model-driven feature extraction and classification, including basic computer vision techniques for extracting high-level semantics from images. A brief overview of probabilistic classification with generative and discriminative classifiers is then provided. Next, neural networks are presented as a means to learn a classification model directly from labeled sample images, with individual components of the network discussed. The relationships between network components and those of a traditional designed model are explored, and different concepts for regularizing model training are explained. Finally, various methods for analyzing what a network has learned are covered in the closing section of the book.The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.
R 3,114.00
Introduction To Machine Learning In Quantitative Finance, An
In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git
R 3,680.93
Introduction to Linear Programming and Game Theory
Praise for the Second Edition: "This is quite a well-done book: very tightly organized, better-than-average exposition, and numerous examples, illustrations, and applications." —Mathematical Reviews of the American Mathematical Society An Introduction to Linear Programming and Game Theory, Third Edition presents a rigorous, yet accessible, introduction to the theoretical concepts and computational techniques of linear programming and game theory. Now with more extensive modeling exercises and detailed integer programming examples, this book uniquely illustrates how mathematics can be used in real-world applications in the social, life, and managerial sciences, providing readers with the opportunity to develop and apply their analytical abilities when solving realistic problems. This Third Edition addresses various new topics and improvements in the field of mathematical programming, and it also presents two software programs, LP Assistant and the Solver add-in for Microsoft Office Excel, for solving linear programming problems. LP Assistant, developed by coauthor Gerard Keough, allows readers to perform the basic steps of the algorithms provided in the book and is freely available via the book's related Web site. The use of the sensitivity analysis report and integer programming algorithm from the Solver add-in for Microsoft Office Excel is introduced so readers can solve the book's linear and integer programming problems. A detailed appendix contains instructions for the use of both applications. Additional features of the Third Edition include: A discussion of sensitivity analysis for the two-variable problem, along with new examples demonstrating integer programming, non-linear programming, and make vs. buy models Revised proofs and a discussion on the relevance and solution of the dual problem A section on developing an example in Data Envelopment Analysis An outline of the proof of John Nash's theorem on the existence of equilibrium strategy pairs for non-cooperative, non-zero-sum games Providing a complete mathematical development of all presented concepts and examples, Introduction to Linear Programming and Game Theory, Third Edition is an ideal text for linear programming and mathematical modeling courses at the upper-undergraduate and graduate levels. It also serves as a valuable reference for professionals who use game theory in business, economics, and management science.
R 4,883.00
Introduction to Concurrency in Programming Languages
Exploring how concurrent programming can be assisted by language-level techniques, Introduction to Concurrency in Programming Languages presents high-level language techniques for dealing with concurrency in a general context. It provides an understanding of programming languages that offer concurrency features as part of the language definition.The book supplies a conceptual framework for different aspects of parallel algorithm design and implementation. It first addresses the limitations of traditional programming techniques and models when dealing with concurrency. The book then explores the current state of the art in concurrent programming and describes high-level language constructs for concurrency. It also discusses the historical evolution of hardware, corresponding high-level techniques that were developed, and the connection to modern systems, such as multicore and manycore processors. The remainder of the text focuses on common high-level programming techniques and their application to a range of algorithms. The authors offer case studies on genetic algorithms, fractal generation, cellular automata, game logic for solving Sudoku puzzles, pipelined algorithms, and more. Illustrating the effect of concurrency on programs written in familiar languages, this text focuses on novel language abstractions that truly bring concurrency into the language and aid analysis and compilation tools in generating efficient, correct programs. It also explains the complexity involved in taking advantage of concurrency with regard to program correctness and performance.
R 5,652.15
Introduction to Fortran 90 for Scientific Computing
Fortran was one of the earliest programming languages and is still the most important language for scientific and engineering computation. It has evolved considerably over the last 35 years and this book provides an introduction to its latest standard: Fortran 90. The general organization of this text is based on a companion volume, An Introduction to FORTRAN for Scientific Computing, which covered Fortran 77 with some discussion of Fortran 90 features. Ortega begins with a general introduction to computing, then introduces the basic constructs of the Fortran language: variables, assignment statements, the IF statement, repetition by DO loops, arrays, functions and subroutines, and formatted input/output. Only the simplest forms of these constructs are introduced, but even these are enough for students to begin writing fairly sophisticated programs. To develop good programming habits early on, Ortega discusses programming techniques--such as top-down step-wise refinement, and the important question of detecting errors--alongside the factual material right from the beginning. By the end of Chapter 3, students will have covered most of Fortran 77 and many of the simpler added features of Fortran 90. In Chapter 4, Ortega addresses the more advanced features of Fortran 90: derived types, modules, interface blocks, overloading, and pointers, and concludes with a summary of how Fortran 77 differs from Fortran 90. Development of this text took place in many forms as a first-year programming course taught at the University of Virginia.
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Introduction to Data Science
An Introduction to Data Science is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language and RStudio® from the ground up. Short chapters allow instructors to group concepts together for a semester course and provide students with manageable amounts of information for each concept. By taking students systematically through the R programming environment, the book takes the fear out of data science and familiarizes students with the environment so they can be successful when performing advanced functions. The authors cover statistics from a conceptual standpoint, focusing on how to use and interpret statistics, rather than the math behind the statistics. This text then demonstrates how to use data effectively and efficiently to construct models, predict outcomes, visualize data, and make decisions. Accompanying digital resources provide code and datasets for instructors and learners to perform a wide range of data science tasks.
R 3,238.20
Introduction to Prescriptive AI
Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.The book kicks off with an introduction to decision intelligence and provides insight into prescriptive AI and how it can be woven into various business strategies and frameworks. You'll then be introduced to different decision intelligence methodologies and how to implement them, along with advantages and limitations of each. Digging deeper, the authors then walk you through how to perform simulations and interpret the results. A full chapter is devoted to embedding decision intelligence processes and outcomes into your business workflow using various applications. The book concludes by exploring different cognitive biases humans are prone to, and how those biases can be eliminated by combining machine and human intelligence.Upon completing this book, you will understand prescriptive AI, tools, and techniques and will be ready to incorporate them into your business workflow.What You Will LearnImplement full-fledged decision intelligence applications using PythonLeverage the tools, techniques, and methodologies for prescriptive AIUnderstand how prescriptive AI can be used in different domains through practical examplesInterpret results and integrate them into your decision makingWho This Book Is ForData Scientists and Machine Learning Engineers, as well as business professionals who want to understand how AI-driven decision intelligence can help grow their business.
R 1,137.71
Introduction to Microcontroller Programming for Power Electronics Control Applications
Microcontroller programming is not a trivial task. Indeed, it is necessary to set correctly the required peripherals by using programming languages like C/C++ or directly machine code. Nevertheless, MathWorks® developed a model-based workflow linked with an automatic code generation tool able to translate Simulink® schemes into executable files. This represents a rapid prototyping procedure, and it can be applied to many microcontroller boards available on the market. Among them, this introductory book focuses on the C2000 LaunchPadTM family from Texas InstrumentsTM to provide the reader basic programming strategies, implementation guidelines and hardware considerations for some power electronics-based control applications. Starting from simple examples such as turning on/off on-board LEDs, Analog-to-Digital conversion, waveform generation, or how a Pulse-Width-Modulation peripheral should be managed, the reader is guided through the settings of the specific MCU-related Simulink® blocks enabled for code translation. Then, the book proposes several control problems in terms of power management of RL and RLC loads (e.g., involving DC-DC converters) and closed-loop control of DC motors. The control schemes are investigated as well as the working principles of power converter topologies needed to drive the systems under investigation. Finally, a couple of exercises are proposed to check the reader’s understanding while presenting a processor-in-the loop (PIL) technique to either emulate the dynamics of complex systems or testing computational performance.Thus, this book is oriented to graduate students of electrical and automation and control engineering pursuing a curriculum in power electronics and drives, as well as to engineers and researchers who want to deepen their knowledge and acquire new competences in the design and implementations of control schemes aimed to the aforementioned application fields. Indeed, it is assumed that the reader is well acquainted with fundamentals of electrical machines and power electronics, as well as with continuous-time modeling strategies and linear control techniques. In addition, familiarity with sampled-data, discrete-time system analysis and embedded design topics is a plus. However, even if these competences are helpful, they are not essential, since this book provides some basic knowledge even to whom is approaching these topics for the first time. Key concepts are developed from scratch, including a brief review of control theory and modeling strategies for power electronic-based systems.
R 2,904.26
Introduction to Java Through Game Development
Interested in learning how to program with Java? Let’s face it, the best way to learn to program is by writing programs. This can be a daunting proposition with the specter of hours of simple command line example programs hanging over your head. Fear not! Now you can learn to program in Java in a fun way by working on video games. With this book, you’ll get to work with three Java game projects and have access to the complete game code for each project, including a full Java game engine. After completing Introduction to Java through Game Development, you’ll be proficient in Java programming, having worked with the language’s fundamental aspects throughout the text, and will be ready to further your Java and game programming expertise with confidence.What You'llMaster the fundamentals of the Java programming languageUse different data structures like arrays, lists, stacks, and queuesUnderstand game programming basics including the main game loopGain experience working with three different game projects via the book’s coding challengesWork with the 2D game engine that powers the book's included games and learn to create your own new game projectsUnderstand advanced Java topics like classes, encapsulation, inheritance, and polymorphismWork with exceptions and how to use debugging techniques to trace through codeSharpen your skills with over a dozen coding challenges that test your abilities with a development task on a real game projectWho This Book Is ForThis book requires little to no programming experience to understand and benefit from the text.
R 1,026.00
Introduction To Machine Learning In Quantitative Finance, An
In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git
R 1,759.00
SymbolicC++:An Introduction to Computer Algebra using Object-Oriented Programming
Symbolic C++: An Introduction to Computer Algebra Using Object-Oriented Programming provides a concise introduction to C++ and object-oriented programming, using a step-by-step construction of a new object-oriented designed computer algebra system - Symbolic C++. It shows how object-oriented programming can be used to implement a symbolic algebra system and how this can then be applied to different areas in mathematics and physics. This second revised edition:- * Explains the new powerful classes that have been added to Symbolic C++. * Includes the Standard Template Library. * Extends the Java section. * Contains useful classes in scientific computation. * Contains extended coverage of Maple, Mathematica, Reduce and MuPAD.
R 1,619.40
Introduction to R for Social Scientists
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology.Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.
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Mathematical Introduction to Linear Programming and Game Theory
Mathematical elegance is a constant theme in this treatment of linear programming and matrix games. Condensed tableau, minimal in size and notation, are employed for the simplex algorithm. In the context of these tableau the beautiful termination theorem of R.G. Bland is proven more simply than heretofore, and the important duality theorem becomes almost obvious. Examples and extensive discussions throughout the book provide insight into definitions, theorems, and applications. There is considerable informal discussion on how best to play matrix games. The book is designed for a one-semester undergraduate course. Readers will need a degree of mathematical sophistication and general tools such as sets, functions, and summation notation. No single college course is a prerequisite, but most students will do better with some prior college mathematics. This thorough introduction to linear programming and game theory will impart a deep understanding of the material and also increase the student's mathematical maturity.
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R 305.00
Correctness-by-Construction Approach to Programming
The focus of this book is on bridging the gap between two extreme methods for developing software. On the one hand, there are texts and approaches that are so formal that they scare off all but the most dedicated theoretical computer scientists. On the other, there are some who believe that any measure of formality is a waste of time, resulting in software that is developed by following gut feelings and intuitions.Kourie and Watson advocate an approach known as “correctness-by-construction,” a technique to derive algorithms that relies on formal theory, but that requires such theory to be deployed in a very systematic and pragmatic way. First they provide the key theoretical background (like first-order predicate logic or refinement laws) that is needed to understand and apply the method. They then detail a series of graded examples ranging from binary search to lattice cover graph construction and finite automata minimization in order to show how it can be applied to increasingly complex algorithmic problems. The principal purpose of this book is to change the way software developers approach their task at programming-in-the-small level, with a view to improving code quality. Thus it coheres with both the IEEE’s Guide to the Software Engineering Body of Knowledge (SWEBOK) recommendations, which identifies themes covered in this book as part of the software engineer’s arsenal of tools and methods, and with the goals of the Software Engineering Method and Theory (SEMAT) initiative, which aims to “refound software engineering based on a solid theory.”
R 4,269.42
Introduction to R for Social Scientists
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology.Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.
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R 945.00
