Bishop Machine Learning

"Pattern Recognition and Machine Learning: Solutions Exercises" by Christopher M. Bishop

"Pattern Recognition and Machine Learning: Solutions Exercises" by Christopher M. Bishop
Information Science and Statistics
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.

"Pattern Recognition and Machine Learning" by Christopher M. Bishop  eBooks & eLearning

Posted by exLib at Nov. 21, 2011
"Pattern Recognition and Machine Learning" by Christopher M. Bishop

"Pattern Recognition and Machine Learning" by Christopher M. Bishop
Information Science and Statistics
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.

Pattern Recognition and Machine Learning (Repost)  eBooks & eLearning

Posted by foosaa at July 22, 2009
Pattern Recognition and Machine Learning (Repost)

Christopher M. Bishop, "Pattern Recognition and Machine Learning"
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.

Computational Trust Models and Machine Learning (Repost)  eBooks & eLearning

Posted by naag at March 29, 2017
Computational Trust Models and Machine Learning (Repost)

Computational Trust Models and Machine Learning By Xin Liu, Anwitaman Datta, Ee-Peng Lim
2014 | 232 Pages | ISBN: 1482226669 | PDF | 2 MB

Mastering Machine Learning with scikit-learn  eBooks & eLearning

Posted by naag at March 28, 2017
Mastering Machine Learning with scikit-learn

Mastering Machine Learning with scikit-learn By Gavin Hackeling
2014 | 238 Pages | ISBN: 1783988363 | EPUB | 6.20 MB
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
English | 2017 | ISBN: 1491962291 | 566 Pages | EPUB | 8.41 MB

Machine Learning and Knowledge Discovery in Databases  eBooks & eLearning

Posted by AvaxGenius at March 26, 2017
Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I By Annalisa AppicePedro Pereira RodriguesVítor Santos CostaCarlos SoaresJoão GamaAlípio Jorge
English | PDF | 2015 | 760 Pages | ISBN : 3319235281 | 26.78 MB

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015.

Visual Quality Assessment by Machine Learning  eBooks & eLearning

Posted by AvaxGenius at March 25, 2017
Visual Quality Assessment by Machine Learning

Visual Quality Assessment by Machine Learning By Long Xu, Weisi Lin, C.-C. Jay Kuo
English | PDF,EPUB | 2015 | 142 Pages | ISBN : 9812874674 | 4.84 MB

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods.

Machine Learning with Python: The Basics  eBooks & eLearning

Posted by naag at March 24, 2017
Machine Learning with Python: The Basics

Machine Learning with Python: The Basics
2017 | English | ASIN: B06XT6M64L | 210 pages | PDF + EPUB (conv) | 0.7 Mb

Mastering .NET Machine Learning  eBooks & eLearning

Posted by readerXXI at March 23, 2017
Mastering .NET Machine Learning

Mastering .NET Machine Learning
by Jamie Dixon
English | 2016 | ISBN: 1785888404 | 358 Pages | PDF | 6.34 MB

This book is targeted at .NET developers who want to build complex machine learning systems. Some basic understanding of data science is required.