Introduction to Statistical Machine Learning

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Morgan Kaufmann, Oct 31, 2015 - Computers - 534 pages
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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.

  • Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus.
  • Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning.
  • Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks
  • Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.
 

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Contents

3 GENERATIVE APPROACH TO STATISTICAL PATTERN RECOGNITION
111
4 DISCRIMINATIVE APPROACH TO STATISTICAL MACHINE LEARNING
235
5 FURTHER TOPICS
341
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Other editions - View all

Introduction to Statistical Machine Learning
Masashi Sugiyama
No preview available - 2015

Common terms and phrases

algorithm approximation autoencoder behavior is illustrated called CHAPTER CONTENTS class-posterior probability computed cross validation decision boundary dimensionality reduction Example expectation explained in Section FIGURE Fisher discriminant analysis Gaussian kernel model Gaussian mixture model Gaussian model Gibbs sampling given hinge loss Huber loss minimization illustrated in Fig inequality iteratively reweighted k=exp KL divergence ℓ2-loss Let us consider linear-in-parameter model log-likelihood LS regression machine learning marginal likelihood MATLAB code maximum likelihood method model selection moment-generating function multitask n)+repmat x2 normal distribution number of parameters number of training obtained optimization problem output parametric model plot posterior probability Pr(x probability density function probability distribution probability mass function provided in Fig random variable rejection sampling repnat samples xi semisupervised ſº statistic stochastic gradient supervised learning support vector classification training samples unsupervised variance variance-covariance matrix X=linspace zero

About the author (2015)

Masashi Sugiyama received the degrees of Bachelor of Engineering, Master of Engineering, and Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan in 1997, 1999, and 2001, respectively. In 2001, he was appointed Assistant Professor in the same institute, and he was promoted to Associate Professor in 2003. He moved to the University of Tokyo as Professor in 2014. He received an Alexander von Humboldt Foundation Research Fellowship and researched at Fraunhofer Institute, Berlin, Germany, from 2003 to 2004. In 2006, he received a European Commission Program Erasmus Mundus Scholarship and researched at the University of Edinburgh, Edinburgh, UK. He received the Faculty Award from IBM in 2007 for his contribution to machine learning under non-stationarity, the Nagao Special Researcher Award from the Information Processing Society of Japan in 2011 and the Young Scientists' Prize from the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan for his contribution to the density-ratio paradigm of machine learning. His research interests include theories and algorithms of machine learning and data mining, and a wide range of applications such as signal processing, image processing, and robot control.

Bibliographic information

QR code for Introduction to Statistical Machine Learning
TitleIntroduction to Statistical Machine Learning
AuthorMasashi Sugiyama
PublisherMorgan Kaufmann, 2015
ISBN0128023503, 9780128023501
Length534 pages
Subjects ›  › 

Computers / Intelligence (AI) & Semantics
Mathematics / Probability & Statistics / General
  
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