Provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures. Offers instructor resources including solutions for exercises and complete set of lecture slides. Assumes only a modest statistics or mathematics background, and no database knowledge is needed. Topics covered include classification, association analysis, clustering, anomaly detection, and avoiding false discoveries.

本书由Michigan State University 的Pang-Ning Tan和University of Minnesota的Michael Steinbach合著，深入浅出地说明了数据挖掘的四大部分:可视化、相关性分析、分类和聚集分析的概念和相关算法。本书同样也是斯坦福大学数据挖掘课程(Stats 202 Data Mining)的教学用书。本书为pdf英文版本，使用快压压缩。

Jian Tao jtao@tamu.edu Spring 2020 HPRC Short Course 03/27/2020 ● Part I. Deep Learning ● Part II. Intro to TensorFlow

Introduction to Parallel Computing From Algorithms toProgramming

本文档主要是一本信息安全专业的电子工具书《An Introduction to Mathematical Cryptography》。本书的内容主要讲解的是信息安全的基本概念，包括有解密解密安全性能评价等等；最重要的是，本书还介绍了相关的加密算法的数学模型，包括有：RSA算法，椭圆曲线算法等等。总而言之，这个电子书资源非常适合相关的信息安全方向的相关学习者。

Understanding the GPS - An Introduction to the Global Positioning System

Introduction to Finite and Spectral Element Methods Using MATLAB®Second Edition This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint.

这是一个实用的分步指南，将帮助您快速成为熟练的GNU Octave用户。 本书中包含清晰的示例，屏幕截图和代码，可以毫无问题地进行数据分析。

IPV6基础培训讲座 1-Introduction-RickGraziani

Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of investment after the global financial crisis in 2008. Today, pension funds and institutional investors are using this approach in the development of smart indexing and the redefinition of long-term investment policies. Written by a well-known expert of asset management and risk parity, Introduction to Risk Parity and Budgeting provides an up-to-date treatment of this alternative method to Markowitz optimization. It builds financial exposure to equities and commodities, considers credit risk in the management of bond portfolios, and designs long-term investment policy. The first part of the book gives a theoretical account of portfolio optimization and risk parity. The author discusses modern portfolio theory and offers a comprehensive guide to risk budgeting. Each chapter in the second part presents an application of risk parity to a specific asset class. The text covers risk-based equity indexation (also called smart beta) and shows how to use risk budgeting techniques to manage bond portfolios. It also explores alternative investments, such as commodities and hedge funds, and applies risk parity techniques to multi-asset classes. The book's first appendix provides technical materials on optimization problems, copula functions, and dynamic asset allocation. The second appendix contains 30 tutorial exercises. Solutions to the exercises, slides for instructors, and Gauss computer programs to reproduce the book's examples, tables, and figures are available on the author's website.