Mathematics of Discrete Structures for Computer Science

This book intends to lead its readers to some of the current topics of research in the geometry of polyhedral surfaces with applications to computer graphics. The main feature of the book is a systema

This book includes the probability of events, discrete random variables and their distribution, continuous random variables and their distribution, digital characteristics of random variables, law of large numbers and central limit theorem, sampling distribution, parameter estimation and hypothesis testing.All of the authors of this book have the background of visiting British and American university. The writing language is easy to understand, and the content has moderate difficulty.This book can be used for the teaching of probability and statistics courses of Sino-foreign cooperation projects and foreign student programs in universities of science and engineering (non-mathematics majors), as well as bilingual teaching of probability and statistics.本書內容包括事件的概率、離散型隨機變量及其分佈、連續型隨機變量及其分佈、隨機變量的數字特徵、大數定律與中心極限定理、抽樣分佈、參數估計和假設檢驗。本書編著者均有英美訪學背景，英文語言簡單易懂，寫作簡約，內容難易適中，便於學習。本書可供理工科大學(非數學專業)中外合作辦學項目和留

《多元復分析導論》內容簡介：The idea for this book came when I was an assistant at the Department of Mathe-matics and Computer Science at the Philipps-University Marburg, Germany. Sev-eral times I faced the task of

This textbook covers a course of computer simulation of proteins for science major students， It consists of two&nb

Linear algebra permeates mathematics,perhaps more so than any other single subject.It plays an essential role in pure and applied mathematics,statistics,computer science,and many aspects of physics and engineering.This book conveys in a user-friendly way the basic and advanced techniques of linear algebra from the point of view of a working analyst.The techniques are illustrated by a wide sample of applications and examples that are chosen to highlight the tools of the trade.In short,this is material that many of us wish we had been taught as graduate students.Roughly the first third of the book covers the basic material of a first course in linear algebra.The remaining chapters are devoted to applications drawn from vector calculus,numerical analysis,control theory,complex analysis,convexity and functional analysis.In particular,fixed point theorems,extremal problems,matrix equations,zero location and eigenvalue location problems,and matrices with nonnegative entries are discussed.App

This book provides a comprehensive foundation of machine learning. To answer the questions of what to learn, how to learn, what to get from learning, and how to evaluate, as well as what is meant by learning, the book focuses on the fundamental basics of machine learning, its methodology, theory, algorithms, and evaluations, together with some philosophical thinking on comparison between machine learning and human learning for machinery intelligence.The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2), Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6), Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and Challenges (Chapter 12).The book can be used as a textbook for college, undergraduate, graduate and PhD students majored in computer science, automation, electronic engineering, communication, ect. It can also be used as

Credlts for Figures and Color Plates Much has changed in the world of fractals, computer graphics and modem mathematics since the first edition of Fractals Everywhere appeared. The company Iterated Sy