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Introduction to Financial Mathematics With Computer Applications

Introduction to Financial Mathematics With Computer Applications

This book’s primary objective is to educate aspiring finance professionals about mathematics and computation in the context of financial derivatives. The authors offer a balance of traditional coverage and technology to fill the void between highly mathematical books and broad finance books. The focus of this book is twofold: To partner mathematics with corresponding intuition rather than diving so deeply into the mathematics that the material is inaccessible to many readers. To build reader intuition understanding and confidence through three types of computer applications that help the reader understand the mathematics of the models. Unlike many books on financial derivatives requiring stochastic calculus this book presents the fundamental theories based on only undergraduate probability knowledge. A key feature of this book is its focus on applying models in three programming languages –R Mathematica and EXCEL. Each of the three approaches offers unique advantages. The computer applications are carefully introduced and require little prior programming background. The financial derivative models that are included in this book are virtually identical to those covered in the top financial professional certificate programs in finance. The overlap of financial models between these programs and this book is broad and deep. | Introduction to Financial Mathematics With Computer Applications

GBP 89.99
1

Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems aims to introduce high school students to all the necessary topics that frequently emerge in international Math Olympiad competitions. In addition to introducing the topics the book will also provide several repetitive-type guided problems to help develop vital techniques in solving problems correctly and efficiently. The techniques employed in the book will help prepare students for the topics they will typically face in an Olympiad-style event but also for future college mathematics courses in Discrete Mathematics Graph Theory Differential Equations Number Theory and Abstract Algebra. Features: Numerous problems designed to embed good practice in readers and build underlying reasoning analysis and problem-solving skills Suitable for advanced high school students preparing for Math Olympiad competitions

GBP 24.99
1

Introduction to NFL Analytics with R

Introduction to NFL Analytics with R

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans and commentators throughout broadcasts regularly use terms such as air yards CPOE and EPA on a casual basis. This rapid growth combined with an increasing accessibility to NFL data has helped create a burgeoning amateur analytics movement highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to opting to provide step-by-step instructions in a structured jargon-free manner. Key Coverage: • Installing R RStudio and necessary packages • Working and becoming fluent in the tidyverse • Finding meaning in NFL data with examples from all the functions in the nflverse family of packages • Using NFL data to create eye-catching data visualizations • Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting The book is written for novices of R programming all the way to more experienced coders as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists bloggers and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.

GBP 52.99
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Introduction to Modeling and Simulation with MATLAB and Python

Introduction to Modeling and Simulation with MATLAB and Python

Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science social science and engineering that wish to learn the principles of computer modeling as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise the authors return to modeling concepts providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way the book builds both modeling and programming expertise with a just-in-time approach so that by the end of the book students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading tutorials and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www. intromodeling. com provides updates to instructions when there are substantial changes in software versions as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors. | Introduction to Modeling and Simulation with MATLAB® and Python

GBP 44.99
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Introduction to Web Interaction Design With HTML and CSS

Introduction to Web Interaction Design With HTML and CSS

This book introduces standard and new HTML5 elements and attributes and CSS3 properties commonly used in Web design as well as design guidelines for their effective use. Its approach of explaining every line of code in the examples it uses to show the usage of the HTML elements and CSS properties introduced makes it an invaluable Web design resource for beginners as well as intermediates looking to fill in gaps in their knowledge. In addition the inclusion of user-centered design process stages and how they are best managed in website development makes the book unique in its area. Also the book's approach of including challenges after each topic to help refresh readers' knowledge as well as make them think ensures that there are ample activities to keep learners motivated and engaged. Key Features Comprehensively covers standard and new HTML5 elements and attributes and CSS3 properties. Includes a lot of challenges/exercises; one after each HTML element or CSS property introduced and demonstrated with examples. Example codes can be copied and pasted as-is to implement and experiment with. For every HTML element or CSS property introduced guidelines are provided where relevant on how to best use them in a design to enhance usability and accessibility. Includes comprehensive explanation of flexible box and grid layout models and how to use them to create responsive and adaptive Web design. Covers the importance of visual aesthetics in design including design elements and principles and examples of how they can be applied in Web design to produce good user experience. Includes comprehensive guidelines on how to design for standard and mobile screens including discussion of touch gesture interaction design and standard gestures and the functions for which they are most commonly used. Introduces the stages of user-centered design process including Web accessibility and user-experience testing and managerial aspects of Web development including intellectual property. Provides a brief introduction on how to make HTML and CSS codes more compact and more efficient and how to combine them with other languages commonly used in Web design and development such as JavaScript AJAX and PHP. | Introduction to Web Interaction Design With HTML and CSS

GBP 59.99
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Introduction to Probability with Mathematica

Introduction to Probability with Mathematica

Updated to conform to Mathematica® 7. 0 Introduction to Probability with Mathematica® Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanyingdownloadable resources offer instructors the option of creating class notes demonstrations and projects. New to the Second EditionExpanded section on Markov chains that includes a study of absorbing chainsNew sections on order statistics transformations of multivariate normal random variables and Brownian motionMore example data of the normal distribution More attention on conditional expectation which has become significant in financial mathematicsAdditional problems from Actuarial Exam PNew appendix that gives a basic introduction to MathematicaNew examples exercises and data sets particularly on the bivariate normal distributionNew visualization and animation features from Mathematica 7. 0Updated Mathematica notebooks on the downloadable resources. After covering topics in discrete probability the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions including normal bivariate normal gamma and chi-square distributions. The author goes on to examine the history of probability the laws of large numbers and the central limit theorem. The final chapter explores stochastic processes and applications ideal for students in operations research and finance.

GBP 59.99
1

Introduction to Computational Models with Python

Introduction to Computational Models with Python

Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website. The book’s five sections present: An overview of problem solving and simple Python programs introducing the basic models and techniques for designing and implementing problem solutions independent of software and hardware toolsProgramming principles with the Python programming language covering basic programming concepts data definitions programming structures with flowcharts and pseudo-code solving problems and algorithmsPython lists arrays basic data structures object orientation linked lists recursion and running programs under LinuxImplementation of computational models with Python using Numpy with examples and case studies The modeling of linear optimization problems from problem formulation to implementation of computational modelsThis book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing including parallel computing using MPI grid computing and other methods and techniques used in high-performance computing.

GBP 44.99
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Introduction to Data Science Data Analysis and Prediction Algorithms with R

Introduction to Data Science Data Analysis and Prediction Algorithms with R

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability statistical inference linear regression and machine learning. It also helps you develop skills such as R programming data wrangling data visualization predictive algorithm building file organization with UNIX/Linux shell version control with Git and GitHub and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary although some experience with programming may be helpful. The book is divided into six parts: R data visualization statistics with R data wrangling machine learning and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state self-reported student heights trends in world health and economics the impact of vaccines on infectious disease rates the financial crisis of 2007-2008 election forecasting building a baseball team image processing of hand-written digits and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course. | Introduction to Data Science Data Analysis and Prediction Algorithms with R

GBP 82.99
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A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2 provides the reader with the concepts and tools necessary to bring a robot to life through programming. It will equip the reader with the skills necessary to undertake projects with ROS2 the new version of ROS. It is not necessary to have previous experience with ROS2 as it will describe its concepts tools and methodologies from the beginning. Key Features Uses the two programming languages officially supported in ROS2 (C++ mainly and Python) Approaches ROS2 from three different but complementary dimensions: the Community Computation Graph and the Workspace Includes a complete simulated robot development and testing strategies Behavior Trees and Nav2 description setup and use A GitHub repository with code to assist readers It will appeal to motivated engineering students engineers and professionals working with robot programming.

GBP 44.99
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An Introduction to Acceptance Sampling and SPC with R

An Introduction to Acceptance Sampling and SPC with R

An Introduction to Acceptance Sampling and SPC with R is an introduction to statistical methods used in monitoring controlling and improving quality. Topics covered include acceptance sampling; Shewhart control charts for Phase I studies; graphical and statistical tools for discovering and eliminating the cause of out-of-control-conditions; Cusum and EWMA control charts for Phase II process monitoring; and the design and analysis of experiments for process troubleshooting and discovering ways to improve process output. Origins of statistical quality control and the technical topics presented in the remainder of the book are those recommended in the ANSI/ASQ/ISO guidelines and standards for industry. The final chapter ties everything together by discussing modern management philosophies that encourage the use of the technical methods presented earlier. In the modern world sampling plans and the statistical calculations used in statistical quality control are done with the help of computers. As an open source high-level programming language with flexible graphical output options R runs on Windows Mac and Linux operating systems and has add-on packages that equal or exceed the capability of commercial software for statistical methods used in quality control. In this book we will focus on several R packages. In addition to demonstrating how to use R for acceptance sampling and control charts this book will concentrate on how the use of these specific tools can lead to quality improvements both within a company and within their supplier companies. This would be a suitable book for a one-semester undergraduate course emphasizing statistical quality control for engineering majors (such as manufacturing engineering or industrial engineering) or a supplemental text for a graduate engineering course that included quality control topics.

GBP 48.99
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Introduction to Financial Derivatives with Python

Introduction to Financial Derivatives with Python

Introduction to Financial Derivatives with Python is an ideal textbook for an undergraduate course on derivatives whether on a finance economics or financial mathematics programme. As well as covering all of the essential topics one would expect to be covered the book also includes the basis of the numerical techniques most used in the financial industry and their implementation in Python. Features Connected to a Github repository with the codes in the book. The repository can be accessed at https://bit. ly/3bllnuf Suitable for undergraduate students as well as anyone who wants a gentle introduction to the principles of quantitative finance No pre-requisites required for programming or advanced mathematics beyond basic calculus

GBP 74.99
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Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation the authors explore how machine learning techniques apply to bioinformatics problems such as electron density map interpretation biclustering DNA sequence analysis and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

GBP 59.99
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Monte Carlo Simulation with Applications to Finance

Monte Carlo Simulation with Applications to Finance

Developed from the author’s course on Monte Carlo simulation at Brown University Monte Carlo Simulation with Applications to Finance provides a self-contained introduction to Monte Carlo methods in financial engineering. It is suitable for advanced undergraduate and graduate students taking a one-semester course or for practitioners in the financial industry. The author first presents the necessary mathematical tools for simulation arbitrary free option pricing and the basic implementation of Monte Carlo schemes. He then describes variance reduction techniques including control variates stratification conditioning importance sampling and cross-entropy. The text concludes with stochastic calculus and the simulation of diffusion processes. Only requiring some familiarity with probability and statistics the book keeps much of the mathematics at an informal level and avoids technical measure-theoretic jargon to provide a practical understanding of the basics. It includes a large number of examples as well as MATLAB® coding exercises that are designed in a progressive manner so that no prior experience with MATLAB is needed.

GBP 59.99
1

Combinatorial Nullstellensatz With Applications to Graph Colouring

Combinatorial Nullstellensatz With Applications to Graph Colouring

Combinatorial Nullstellensatz is a novel theorem in algebra introduced by Noga Alon to tackle combinatorial problems in diverse areas of mathematics. This book focuses on the applications of this theorem to graph colouring. A key step in the applications of Combinatorial Nullstellensatz is to show that the coefficient of a certain monomial in the expansion of a polynomial is nonzero. The major part of the book concentrates on three methods for calculating the coefficients: Alon-Tarsi orientation: The task is to show that a graph has an orientation with given maximum out-degree and for which the number of even Eulerian sub-digraphs is different from the number of odd Eulerian sub-digraphs. In particular this method is used to show that a graph whose edge set decomposes into a Hamilton cycle and vertex-disjoint triangles is 3-choosable and that every planar graph has a matching whose deletion results in a 4-choosable graph. Interpolation formula for the coefficient: This method is in particular used to show that toroidal grids of even order are 3-choosable r-edge colourable r-regular planar graphs are r-edge choosable and complete graphs of order p+1 where p is a prime are p-edge choosable. Coefficients as the permanents of matrices: This method is in particular used in the study of the list version of vertex-edge weighting and to show that every graph is (2 3)-choosable. It is suited as a reference book for a graduate course in mathematics. | Combinatorial Nullstellensatz With Applications to Graph Colouring

GBP 52.99
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A Systematic Approach to Learning Robot Programming with ROS

A Systematic Approach to Learning Robot Programming with ROS

A Systematic Approach to Learning Robot Programming with ROS provides a comprehensive introduction to the essential components of ROS through detailed explanations of simple code examples along with the corresponding theory of operation. The book explores the organization of ROS how to understand ROS packages how to use ROS tools how to incorporate existing ROS packages into new applications and how to develop new packages for robotics and automation. It also facilitates continuing education by preparing the reader to better understand the existing on-line documentation. The book is organized into six parts. It begins with an introduction to ROS foundations including writing ROS nodes and ROS tools. Messages Classes and Servers are also covered. The second part of the book features simulation and visualization with ROS including coordinate transforms. The next part of the book discusses perceptual processing in ROS. It includes coverage of using cameras in ROS depth imaging and point clouds and point cloud processing. Mobile robot control and navigation in ROS is featured in the fourth part of the book The fifth section of the book contains coverage of robot arms in ROS. This section explores robot arm kinematics arm motion planning arm control with the Baxter Simulator and an object-grabber package. The last part of the book focuses on system integration and higher-level control including perception-based and mobile manipulation. This accessible text includes examples throughout and C++ code examples are also provided at https://github. com/wsnewman/learning_ros

GBP 62.99
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MATLAB Handbook with Applications to Mathematics Science Engineering and Finance

MATLAB Handbook with Applications to Mathematics Science Engineering and Finance

The purpose of this handbook is to allow users to learn and master the mathematics software package MATLAB® as well as to serve as a quick reference to some of the most used instructions in the package. A unique feature of this handbook is that it can be used by the novice and by experienced users alike. For experienced users it has four chapters with examples and applications in engineering finance physics and optimization. Exercises are included along with solutions available for the interested reader on the book’s web page. These exercises are a complement for the interested reader who wishes to get a deeper understanding of MATLAB. FeaturesCovers both MATLAB and introduction to SimulinkCovers the use of GUIs in MATLAB and SimulinkOffers downloadable examples and programs from the handbook’s websiteProvides an introduction to object oriented programming using MATLABIncludes applications from many areasIncludes the realization of executable files for MATLAB programs and Simulink models | MATLAB Handbook with Applications to Mathematics Science Engineering and Finance

GBP 44.99
1

Mathematical Modeling using Fuzzy Logic Applications to Sustainability

Mathematical Modeling using Fuzzy Logic Applications to Sustainability

Mathematical Modeling using Fuzzy Logic has been a dream project for the author. Fuzzy logic provides a unique method of approximate reasoning in an imperfect world. This text is a bridge to the principles of fuzzy logic through an application-focused approach to selected topics in engineering and management. The many examples point to the richer solutions obtained through fuzzy logic and to the possibilities of much wider applications.  There are relatively very few texts available at present in fuzzy logic applications. The style and content of this text is complementary to those already available. New areas of application like application of fuzzy logic in modeling of sustainability are presented in a graded approach in which the underlying concepts are first described. The text is broadly divided into two parts: the first treats processes materials and system applications related to fuzzy logic and the second delves into the modeling of sustainability with the help of fuzzy logic. This book offers comprehensive coverage of the most essential topics including: Treating processes materials system applications related to fuzzy logic Highlighting new areas of application of fuzzy logic Identifying possibilities of much wider applications of fuzzy logic Modeling of sustainability with the help of fuzzy logic The level enables a selection of the text to be made for the substance of undergraduate- graduate- and postgraduate-level courses. There is also sufficient volume and quality for the basis of a postgraduate course. A more restricted and judicious selection can provide the material for a professional short course and various university-level courses. | Mathematical Modeling using Fuzzy Logic Applications to Sustainability

GBP 105.00
1

Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Machine Learning a branch of Artificial Intelligence is influencing the society industry and academia at large. The adaptability of Python programming language to Machine Learning has increased its popularity further. Another technology on the horizon is Internet of Things (IoT). The present book tries to address IoT Python and Machine Learning along with a small introduction to Image Processing. If you are a novice programmer or have just started exploring IoT or Machine Learning with Python then this book is for you. Features: Raspberry Pi as IoT is described along with the procedure for installation and configuration. A simple introduction to Python Programming Language along with its popular library packages like NumPy Pandas SciPy and Matplotlib are dealt in an exhaustive manner along with relevant examples. Machine Learning along with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT is introduced to the audience who love to apply Machine Learning algorithms to Images The book follows hands-on approach and provide a huge collection of Python programs. | Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

GBP 44.99
1

Medical Risk Prediction Models With Ties to Machine Learning

Medical Risk Prediction Models With Ties to Machine Learning

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians epidemiologists and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination calibration and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research. | Medical Risk Prediction Models With Ties to Machine Learning

GBP 48.99
1

Handbook of Statistical Distributions with Applications

Handbook of Statistical Distributions with Applications

Easy-to-Use Reference and Software for Statistical Modeling and TestingHandbook of Statistical Distributions with Applications Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations. Along with many new examples and results this edition includes both the author’s StatCalc software and R codes to accurately and easily carry out computations. New to the Second Edition Major changes in binomial Poisson normal gamma Weibull exponential logistic Laplace and Pareto distributions Updated statistical tests and intervals based on recent publications in statistical journals Enhanced PC calculator StatCalc with electronic help manualsR functions for cases where StatCalc is not applicable with the codes available onlineThis highly praised handbook integrates popular probability distribution models formulas applications and software to help you compute a variety of statistical intervals. It covers probability and percentiles algorithms for random number generation hypothesis tests confidence intervals tolerance intervals prediction intervals sample size determination and much more.

GBP 44.99
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Introduction to Bioinformatics with R A Practical Guide for Biologists

Introduction to Bioinformatics with R A Practical Guide for Biologists

In biological research the amount of data available to researchers has increased so much over recent years it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required readers will develop the ability to plan suitable analyses of biological datasets and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained including linear and rank-based correlation distance metrics and hierarchical clustering hypothesis testing using linear regression proportional hazards regression for survival data and principal component analysis. These methods are then applied as appropriate throughout the case studies illustrating how they can be used to answer research questions. Key Features: · Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. · Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook from basic principles · Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves. · Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq genome sequencing ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens. · Gives worked-out examples geared towards problems encountered in cancer research which can also be applied across many areas of molecular biology and medical research. This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills. | Introduction to Bioinformatics with R A Practical Guide for Biologists

GBP 52.99
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Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
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Smooth Manifolds and Fibre Bundles with Applications to Theoretical Physics

Smooth Manifolds and Fibre Bundles with Applications to Theoretical Physics

This book provides a systematic presentation of the mathematical foundation of modern physics with applications particularly within classical mechanics and the theory of relativity. Written to be self-contained this book provides complete and rigorous proofs of all the results presented within. Among the themes illustrated in the book are differentiable manifolds differential forms fiber bundles and differential geometry with non-trivial applications especially within the general theory of relativity. The emphasis is upon a systematic and logical construction of the mathematical foundations. It can be used as a textbook for a pure mathematics course in differential geometry assuming the reader has a good understanding of basic analysis linear algebra and point set topology. The book will also appeal to students of theoretical physics interested in the mathematical foundation of the theories. | Smooth Manifolds and Fibre Bundles with Applications to Theoretical Physics

GBP 44.99
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An Introduction to Excel VBA Programming with Applications in Finance and Insurance

An Introduction to Excel VBA Programming with Applications in Finance and Insurance

Excel Visual Basic for Applications (VBA) can be used to automate operations in Excel and is one of the most frequently used software programs for manipulating data and building models in banks and insurance companies. An Introduction to Excel VBA Programming: with Applications in Finance and Insurance introduces readers to the basic fundamentals of VBA Programming while demonstrating applications of VBA to solve real-world problems in finance and insurance. Assuming no prior programming experience and with reproducible examples using code and data this text is suitable for advanced undergraduate students graduate students actuaries and financial analysts who wish to learn VBA. Features: Presents the theory behind the algorithms in detail Includes more than 100 exercises with selected solutions Provides VBA code in Excel files and data to reproduce the results in the book Offers a solutions manual for qualified instructors | An Introduction to Excel VBA Programming with Applications in Finance and Insurance

GBP 44.99
1