Pattern Recognition and Machine Learning

✍ Scribed by Christopher M. Bishop

• Publisher: Springer
• Year: 2011
• Tongue: English
• Leaves: 803
• Edition: Hardcover
• Category: Library

✦ Synopsis

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. In particular, 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 models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This 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, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

✦ Table of Contents

Pattern Recognition and Machine Learning_Christopher M. Bishop (Springer 2006 703s)......Page 1
Information Science and Statistics......Page 2
Pattern Recognition and Machine Learning......Page 3
Preface......Page 6
Mathematical notation......Page 9
Contents......Page 11
1 Introduction......Page 19
2 Probability Distributions......Page 85
3 Linear Models for Regression......Page 155
4 Linear Models for Classification......Page 196
5 Neural Networks......Page 242
6 Kernel Methods......Page 308
7 Sparse Kernel Machines......Page 341
8 Graphical Models......Page 375
9 Mixture Models and EM......Page 439
10 Approximate Inference......Page 476
11 Sampling Methods......Page 538
13 Sequential Data......Page 574
14 Combining Models......Page 622
Appendix A. Data Sets......Page 646
Appendix B. Probability Distributions......Page 653
Appendix C. Properties of Matrices......Page 662
Appendix D. Calculus of Variations......Page 669
Appendix E. Lagrange Multipliers......Page 672
References......Page 676
Index......Page 694
Pattern Recognition and Machine Learning (Solutions to the Exercises 2007 100s) _Christopher M. Bishop......Page 704
Contents......Page 708
Chapter 1 Pattern Recognition......Page 710
Chapter 2 Density Estimation......Page 722
Chapter 3 Linear Models for Regression......Page 737
Chapter 4 Linear Models for Classification......Page 744
Chapter 5 Neural Networks......Page 749
Chapter 6 Kernel Methods......Page 756
Chapter 7 Sparse Kernel Machines......Page 762
Chapter 8 Probabilistic Graphical Models......Page 766
Chapter 9 Mixture Models......Page 771
Chapter 10 Variational Inference and EM......Page 775
Chapter 11 Sampling Methods......Page 785
Chapter 12 Latent Variables......Page 787
Chapter 13 Sequential Data......Page 794
Chapter 14 Combining Models......Page 798