Pattern Recognition and Machine Learning

The dramatic growth of practical applications for machine learning over the past decade has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have gone from being a specialized niche to becoming 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 variety of approximate inference algorithms, such as Variational Bayesian and Expectation Propagation, while new kernel-based models have had a significant impact on both algorithms and applications. .

This brand new textbook reflects these recent developments by providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduate students or first-year graduate students, as well as researchers and professionals. No prior knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience using probability would be helpful, but not essential, as the book includes a separate introduction to basic probability theory.

The book is suitable for courses in machine learning, statistics, computer science, signal processing, computer vision, data mining and bioinformatics. Extensive support is provided to the course instructors, including over 400 exercises, categorized by difficulty. Examples of solutions for a subset of the exercises are available on the book’s website, while solutions for the rest can be obtained from the faculty at the publisher. The book is supported by a wide range of additional material and the reader is encouraged to visit the book’s website for the latest information.

Coming soon:

*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked “www” in the text)

*For instructors, worked solutions to remaining exercises from the Springer web site

*Lecture slides to accompany each chapter

*Data sets available for download

Pattern Recognition and Machine Learning PDF Free Download

Book Description

This is the first book on pattern recognition presenting the Bayesian view. The book presents algorithms for approximate inference that allow quick approximate answers in situations where exact answers are not feasible.
It uses graphical models to describe probability distributions while no other book applies graphical models to machine learning. No prior knowledge of pattern recognition or machine learning concepts is assumed.
Knowledge of multivariable calculus and basic linear algebra is required, and some experience in the use of probability would be helpful but not necessary as the book includes a self-contained introduction to the theory. basic probability theory.
No prior knowledge of pattern recognition or machine learning concepts is assumed. Knowledge of multivariate analysis and basic linear algebra is required, and some experience in using probability would be helpful but not necessary as the book includes a self-contained introduction to the theory. basic probability.

About Author

Christopher M. Bishop is the Laboratory Director at Microsoft Research Cambridge, Professor of Computer Science at the University of Edinburgh and a Fellow of Darwin College, Cambridge.

Table of contents :

Pattern Recognition and Machine Learning……Page 4
Preface……Page 7
Mathematical notation……Page 10
Contents……Page 12
1. Introduction ……Page 20
2. Probability Distributions ……Page 86
3. Linear Models for Regression ……Page 156
4. Linear Models for Classification ……Page 197
5. Neural Networks ……Page 243
6. Kernel Methods ……Page 309
7. Sparse Kernel Machines ……Page 342
8. Graphical Models ……Page 376
9. Mixture Models and EM ……Page 440
10. Approximate Inference ……Page 477
11. Sampling Methods ……Page 539
12. Continuous Latent Variables ……Page 575
13. Sequential Data ……Page 620
14. Combining Models ……Page 668
Appendix A. Data Sets ……Page 692
Appendix B. Probability Distributions ……Page 699
Appendix C. Properties of Matrices ……Page 708
Appendix D. Calculus of Variations ……Page 715
Appendix E. Lagrange Multipliers ……Page 718
References ……Page 722
Index ……Page 740

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Pattern Recognition and Machine Learning

Author(s): Christopher M. Bishop

Series: Information science and statistics

Publisher: Springer, Year: 2006

ISBN: 9780387310732,0387310738


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