作者: Thomas H. Cormen / Charles E. Leiserson / Ronald L. Rivest / Clifford Stein 出版社: The MIT Press 出版年: 2009-7-31 页数: 1312 定价: USD 92.00 装帧: Hardcover ISBN: 9780262033848 内容简介 · · · · · · 　　Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. 　　The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, and substantial additions to the chapter on recurrences (now called "Divide-and-Conquer"). It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. 　　As of the third edition, this textbook is published exclusively by the MIT Press.

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Title: Introduction to Machine Learning, 3rd Edition Author: Ethem Alpaydin Length: 640 pages Edition: 3rd Language: English Publisher: The MIT Press Publication Date: 2014-08-22 ISBN-10: 0262028182 ISBN-13: 9780262028189 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning method

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The first edition of this book was conceived as a result of my experience writing and maintaining large Fortran programs in both the defense and geophysical fields. During my time in industry, it became obvious that the strategies and techniques re- quired to write large, maintainable Fortran programs were quite different from what new engineers were learning in their Fortran programming classes at school. The in- credible cost of maintaining and modifying large programs once they are placed into service absolutely demands that they be written to be easily understood and modified by people other than their original programmers. My goal for this book is to teach si- multaneously both the fundamentals of the Fortran language and a programming style that results in good, maintainable programs. In addition, it is intended to serve as a reference for graduates working in industry.

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