Matematikprogrammer og statistikprogrammer

This book prepares students to execute the quantitative and computational needs of the finance industry. The quantitative methods are explained in detail with examples from real financial problems like option pricing, risk management, portfolio selection, etc. Codes are provided in R programming language to execute the methods. Tables and figures, often with real data, illustrate the codes. References to related work are intended to aid the reader to pursue areas of specific interest in further detail. The comprehensive background with economic, statistical, mathematical, and computational theory strengthens the understanding. The coverage is broad, and linkages between different sections are explained. The primary audience is graduate students, while it should also be accessible to advanced undergraduates. Practitioners working in the finance industry will also benefit.

An alternate to Mathematica, MATLAB and Maple Key Features Book deals with the most basic algorithms in the area.Book presents several of the most celebrated algorithms in a simple way by omitting obscuring details and separating algorithmic structure from combinatorial theoretical background. It reflects the relationships between applications of text-algorithmic techniques. Classification of algorithms according to the measures of complexity considered. Functions written in an easy and reader-friendly way.About two hundred examples illustrate nicely the behaviour of very complex algorithms. Description Symbolic computation is an essential technique for executing simple to complex mathematical analysis and modeling, not only for scientific and engineering simulations but also for financial calculations. There are plenty of computational tools are available for numerical analysis, but few packages are existing for symbolic computations such as Mathematica, Maple and Matlab are exceptionally powerful tools for symbolic computations, but having huge price tags.Maxima is an open source freeware program. It has user -friendly interface with ample built-in math analytical tools and graphical functions. It can be easily installed in most of the operating systems and proficient in executing both symbolic as well as numerical computations.Maxima has rich mathematical functions and hence has a steep learning curve. But this book is written in a simple style, as a beginner's guide and outlines the basic functions with appropriate examples that are easy to understand.What will you learnCell structure, Annotations LaTex, MathMl format, File format Basic operators, functions Who this book is for IT professionals, undergraduate and postgraduate engineering students, researchers Table of Contents 1. Cell structure2. Annotations3. Input and output4. Graphical User Interface (GUI)5. LaTex and MathMl format6. File format7. Basic operators8. Basic functionsAbout the Author Dr. M Kanagasabapathy is currently working as the Assistant Professor in the Department of Chemistry, Rajapalayam Rajus' College, Rajapalayam (TN) India. His fields of research interests are numerical analysis and modeling for electrochemical processes as well as electrochemical biosensors. He is pursuing electrochemical research works in association with National and International Research Institutes and Universities. He has more than twenty three years of teaching experience in both chemistry as well as chemical engineering disciplines. He has written books on rechargeable batteries and mathematical modeling. He had served as technical consultant for two electrochemical industries.

You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Get your copy NOW!!Why this guide is the best one for Data Scientist? Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics. A simple language has been used.Many examples have been given, both theoretically and programmatically.Screenshots showing program outputs have been added. The book is written chronologically, in a step-by-step manner.Book Objectives: The Aims and Objectives of the Book: To help you understand the basics of machine learning and deep learning.Understand the various categories of machine learning algorithms.To help you understand how different machine learning algorithms work.You will learn how to implement various machine learning algorithms programmatically in Python.To help you learn how to use Scikit-Learn and TensorFlow Libraries in Python.To help you know how to analyze data programmatically to extract patterns, trends, and relationships between variables.Who this Book is for?Here are the target readers for this book: Anybody who is a complete beginner to machine learning in Python.Anybody who needs to advance their programming skills in Python for machine learning programming and deep learning.Professionals in data science.Professors, lecturers or tutors who are looking to find better ways to explain machine learning to their students in the simplest and easiest way.Students and academicians, especially those focusing on neural networks, machine learning, and deep learning.What do you need for this Book? You are required to have installed the following on your computer: Python 3.XNumpyPandasMatplotlibThe Author guides you on how to install the rest of the Python libraries that are required for machine learning and deep learning. What is inside the book: Getting Started Environment Setup Using Scikit-Learn Linear Regression with Scikit-Learn k-Nearest Neighbors Algorithm K-Means Clustering Support Vector Machines Neural Networks with Scikit-learn Random Forest Algorithm Using TensorFlow Recurrent Neural Networks with TensorFlow Linear Classifier This book will teach you machine learning classifiers using scikit-learn and tenserflow . The book provides a great overview of functions you can use to build a support vector machine, decision tree, perceptron, and k-nearest neighbors. Thanks of this book you will be able to set up a learning pipeline that handles input and output data, pre-processes it, selects meaningful features, and applies a classifier on it. This book offers a lot of insight into machine learning for both beginners, as well as for professionals, who already use some machine learning techniques. Concepts and the background of these concepts are explained clearly in this tutorial.

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.MATLAB Differential Equations introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to work on differential equations using MATLAB. It includes techniques for solving ordinary and partial differential equations of various kinds, and systems of such equations, either symbolically or using numerical methods (Euler’s method, Heun’s method, the Taylor series method, the Runge–Kutta method,…). It also describes how to implement mathematical tools such as the Laplace transform, orthogonal polynomials, and special functions (Airy and Bessel functions), and find solutions of finite difference equations.

Clifford algebras are at a crossing point in a variety of research areas, including abstract algebra, crystallography, projective geometry, quantum mechanics, differential geometry and analysis. For many researchers working in this field in ma- thematics and physics, computer algebra software systems have become indispensable tools in theory and applications. This edited survey book consists of 20 chapters showing application of Clifford algebra in quantum mechanics, field theory, spinor calculations, projective geometry, Hypercomplex algebra, function theory and crystallography. Many examples of computations performed with a variety of readily available software programs are presented in detail, i.e., Maple, Mathematica, Axiom, etc. A key feature of the book is that it shows how scientific knowledge can advance with the use of computational tools and software.

This book guides learners through a variety of proofs and applications of the Pythagorean theorem, which has fascinated amateur and professional mathematicians from U.S. President James Garfield to Hindu mathematician Bhaskara since the beginning of recorded history.

This book was written to provide resource materials for teachers to use in their introductory or intermediate statistics class. The  chapter content is ordered along the lines of many popular statistics books so it should be easy to supplement the content and exercises with class lecture materials. The book contains R script programs to demonstrate important topics and concepts covered in a statistics course, including probability, random sampling, population distribution types, role of the Central Limit Theorem, creation of sampling distributions for statistics, and more. The chapters contain T/F quizzes to test basic knowledge of the topics covered. In addition, the book chapters contain numerous exercises with answers or solutions to the exercises provided.  The chapter exercises reinforce an understanding of the statistical concepts presented in the chapters. An instructor can select any of the supplemental materials to enhance lectures and/or provide additional coverage of concepts and topics in their statistics book.

Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.