Introduction to statistical machine learning

Author(s)

• 杉山, 将 スギヤマ, マサシ

Bibliographic Information

Introduction to statistical machine learning

Masashi Sugiyama

Morgan Kaufmann, c2016

• : pbk

Note

Authors' biography: p. xxxi

Includes bibliographical references and index

Description and Table of Contents

Description

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.

Table of Contents

Part I: Introduction to Statistics and Probability1. Random variables and probability distributions2. Examples of discrete probability distributions3. Examples of continuous probability distributions4. Multi-dimensional probability distributions5. Examples of multi-dimensional probability distributions6. Random sample generation from arbitrary probability distributions7. Probability distributions of the sum of independent random variables8. Probability inequalities9. Statistical inference10. Hypothesis testing Part II: Generative Approach to Statistical Pattern Recognition11. Fundamentals of statistical pattern recognition12. Criteria for developing classifiers13. Maximum likelihood estimation14. Theoretical properties of maximum likelihood estimation15. Linear discriminant analysis16. Model selection for maximum likelihood estimation17. Maximum likelihood estimation for Gaussian mixture model18. Bayesian inference19. Numerical computation in Bayesian inference20. Model selection in Bayesian inference21. Kernel density estimation22. Nearest neighbor density estimation Part III: Discriminative Approach to Statistical Machine Learning23. Fundamentals of statistical machine learning24. Learning Models25. Least-squares regression26. Constrained least-squares regression27. Sparse regression28. Robust regression29. Least-squares classification30. Support vector classification31. Ensemble classification32. Probabilistic classification33. Structured classification Part IV: Further Topics34. Outlier detection35. Unsupervised dimensionality reduction36. Clustering37. Online learning38. Semi-supervised learning39. Supervised dimensionality reduction40. Transfer learning41. Multi-task learning

by "Nielsen BookData"

Details

• NCID: BB20260140
• ISBN:
  • 9780128021217
• Country Code: ne
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Amsterdam ; Tokyo
• Pages/Volumes: xxxiii, 497 p.
• Size: 24 cm
• Classification:
  • DC23 : 006.31
• Subject Headings:
  • LCSH : Machine learning -- Statistical methods