Semester-Plus-C04-Sem 1


Book Description

SemesterPlus is an engaging and interactive series of 10 books covering English, Maths, Science/EVS, Social Studies and General Knowledge. The entire syllabi is judiciously and evenly distributed into semesters in each grade. The series combines theoretical learning with a practical, participative and hands-on approach.




Semester-Plus-C04-Sem 2


Book Description

SemesterPlus is an engaging and interactive series of 10 books covering English, Maths, Science/EVS, Social Studies and General Knowledge. The entire syllabi is judiciously and evenly distributed into semesters in each grade. The series combines theoretical learning with a practical, participative and hands-on approach.




MATH 221 FIRST Semester Calculus


Book Description

MATH 221 FIRST Semester CalculusBy Sigurd Angenent




Beginning C++ Programming


Book Description

Modern C++ at your fingertips! About This Book This book gets you started with the exciting world of C++ programming It will enable you to write C++ code that uses the standard library, has a level of object orientation, and uses memory in a safe and effective way It forms the basis of programming and covers concepts such as data structures and the core programming language Who This Book Is For A computer, an internet connection, and the desire to learn how to code in C++ is all you need to get started with this book. What You Will Learn Get familiar with the structure of C++ projects Identify the main structures in the language: functions and classes Feel confident about being able to identify the execution flow through the code Be aware of the facilities of the standard library Gain insights into the basic concepts of object orientation Know how to debug your programs Get acquainted with the standard C++ library In Detail C++ has come a long way and is now adopted in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not to forget its importance in game programming. Despite its strengths in these areas, beginners usually tend to shy away from learning the language because of its steep learning curve. The main mission of this book is to make you familiar and comfortable with C++. You will finish the book not only being able to write your own code, but more importantly, you will be able to read other projects. It is only by being able to read others' code that you will progress from a beginner to an advanced programmer. This book is the first step in that progression. The first task is to familiarize you with the structure of C++ projects so you will know how to start reading a project. Next, you will be able to identify the main structures in the language, functions, and classes, and feel confident being able to identify the execution flow through the code. You will then become aware of the facilities of the standard library and be able to determine whether you need to write a routine yourself, or use an existing routine in the standard library. Throughout the book, there is a big emphasis on memory and pointers. You will understand memory usage, allocation, and access, and be able to write code that does not leak memory. Finally, you will learn about C++ classes and get an introduction to object orientation and polymorphism. Style and approach This straightforward tutorial will help you build strong skills in C++ programming, be it for enterprise software or for low-latency applications such as games or embedded programming. Filled with examples, this book will take you gradually up the steep learning curve of C++.




Probability and Statistics


Book Description

Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.




A Book of Abstract Algebra


Book Description

Accessible but rigorous, this outstanding text encompasses all of the topics covered by a typical course in elementary abstract algebra. Its easy-to-read treatment offers an intuitive approach, featuring informal discussions followed by thematically arranged exercises. This second edition features additional exercises to improve student familiarity with applications. 1990 edition.




All of Statistics


Book Description

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.




Mathematics for Machine Learning


Book Description

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.




Introduction to Applied Linear Algebra


Book Description

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.




Advanced Engineering Mathematics


Book Description