Posted by **exLib** at Nov. 21, 2011

Sрringеr Science+Business Media | 2006 | ISBN: 0387310732 0387310738 | 101 pages | PDF | 1 MB

Example solutions for a subset of the exercises are available from the book. This issue is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. A volume deals with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets.

Posted by **exLib** at Nov. 21, 2011

Sрringеr Science+Business Media | 2006 | ISBN: 0387310738 9780387310732 | 761 pages | PDF | 5 MB

This textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. 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.

Posted by **foosaa** at July 22, 2009

Springer | 2007 | ISBN: 0387310738 | English | 738 Pages | PDF | 9.5 MB

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 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.

Posted by **ksveta6** at Feb. 18, 2017

2013 | ISBN: 3642378455, 3642437621 | English | 321 pages | EPUB | 7 MB

Posted by **hill0** at Feb. 18, 2017

English | 6 Dec. 2016 | ISBN: 3662538059 | 80 Pages | PDF | 2.87 MB

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016.

Posted by **interes** at Feb. 16, 2017

English | 2016 | ISBN: 149196460X | 300 pages | True PDF | 8,5 MB

Posted by **AlenMiler** at Feb. 16, 2017

English | 14 Feb. 2017 | ISBN: 1786462168 | 370 Pages | EPUB/PDF (conv) | 9.27 MB

Posted by **naag** at Feb. 16, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 1.09 GB

Posted by **AvaxGenius** at Feb. 13, 2017

English |PDF | 2015 | 317 Pages | ISBN : 1849199787 | 16.26 MB

This book provides a snapshot of the state of current research at the interface between machine learning and healthcare with special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes.

Posted by **libr** at Feb. 12, 2017

English | ISBN: 1783288515 | 2015 | 329 pages | EPUB | 5,7 MB