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.
- 全面介绍贝叶斯方法在机器学习中的应用
- 将模式识别与机器学习统一于概率框架下
- 涵盖图形模型及变分贝叶斯等前沿技术
- 具备线性代数与微积分基础的进阶学习者
- 希望深入理解机器学习理论本质的研究者
- 从事人工智能与数据挖掘领域的专业人士
- 数学推导严谨，需耐心阅读并动手推导公式
- 全书侧重贝叶斯视角，非初学者首选入门书
- 建议配合习题答案及社区笔记辅助理解难点
- 被誉为机器学习领域的圣经级经典教材
- 理论深度极高，适合查阅而非快速通读
- 部分读者反映逻辑跳跃，需一定数学功底
本导读基于书籍简介、目录、原文摘录、短评和书评生成，不等同于全文精读。
- "Clearly, there are many possible probabilistic structures that can be constructed according to the needs of particular applications. Graphical models provide a general technique for motivating, describing, and analysing such structures, and variational methods provide a powerful framework for perfor"
- "A further issue in finding maximum likelihood solutions arises from the fact that for any given maximum likelihood solution, a K-component mixture will have a total of K! equivalent solutions corresponding to the K! ways of assigning K sets of parameters to K components."
- "The average amount of information that they transmit in the process is obtained by taking the expectation with respect to the distribution p(x) ."
- "Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years."
- "Because the error function is a quadratic function of the coefficients w, its derivatives with respect to the coefficients will be linear in the elements of w, and so the minimization of the error function has a unique solution, denoted by w, which can be found in closed form."
- "RSS(β) is a quadratic function of the parameters, and hence its minimum always exists, but may not be unique."
- "One way to view a linear classification model is in terms of dimensionality reduction."
- "... the internal nodes represent deterministic variables rather than stochastic ones."
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