pattern
Patterns of Culture 豆瓣
作者: Ruth Benedict 出版社: Houghton Mifflin 1989 - 6
A remarkable introduction to cultural studies as relevant today as it was in 1934, Ruth Benedict's groundbreaking study is the book that first brought the concept of "culture" to lay readers. In this fascinating work, Benedict compares the cultures of three peoples: the Kwakiutl of western Canada, the Zuni of the southwestern United States, and the Dobuans of Melanesia. Featuring an introduction by Franz Boas, a preface by Margaret Mead, and a foreword by Mary Catherine Bateson, Patterns of Culture shows the importance culture has on everyday life.
Pattern Recognition and Machine Learning 豆瓣 Goodreads
Pattern Recognition and Machine Learning (Information Science and Statistics)
9.8 (19 个评分) 作者: Christopher Bishop 出版社: Springer 2007 - 10
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.