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逻辑和代数是计算机科学的两大理论基础。数理逻辑各个分支中的许多方面和计算机科学有着密切的联系。本书是作者在多年给硬士研究生讲授《数理逻辑》课程讲义的基础上编写面成的． 本书系统地介绍了逻辑演算，模型论和证明与反驳等数理逻辑的基本内容。全书分为五章，选材时充分考虑了适应逻辑系统的特征和计算机科学的要求_第一章介绍了形式系统的定义，结构及基本概念。第二章介绍了命题逻辑形式系统和消解原理．第三章和第四章分别介绍了一阶逻辑形式系统和带等词的一阶逻辑形式系统，以及模型论的初步知识。其中对形式系统解释的定义采用了更适合描述程序语义的方式，而不是传统的方式。第五章重点讨论证明一阶逻辑中定理的实践问题，给出了多种方法以提高证明效率9同时给出了Herbrand定理及有关结果。此乃人工智能中机械定理证明的理论基础。全书论述严谨详细，并配有一定数量的例题和习题，便于教学和自学． 由于本书在许多地方使用了集合论的概念、术语及记号，因此要求读者有集合论基础知识。若读者已预修《离散数学》课程，那么阅读和学习本书会更容易些。

数据科学入门经典教材：Computer science as an academic discipline began in the 60's. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered nite automata, regular expressions, context free languages, and computability. In the 70's, algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on applications. There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect and store data in the natural sciences, in commerce, and in other elds calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks, which are by far the largest such structures, presents both opportunities and challenges for theory.