Bayesian Reasoning And Machine Learning

Bayesian Reasoning and Machine Learning (repost)  eBooks & eLearning

Posted by roxul at May 24, 2017
Bayesian Reasoning and Machine Learning (repost)

David Barber, "Bayesian Reasoning and Machine Learning"
English | ISBN: 0521518148 | 2012 | 708 pages | PDF | 11 MB

Bayesian Reasoning and Machine Learning  eBooks & eLearning

Posted by nebulae at May 19, 2014
Bayesian Reasoning and Machine Learning

David Barber, "Bayesian Reasoning and Machine Learning"
English | ISBN: 0521518148 | 2012 | 708 pages | PDF | 11 MB

Covariances in Computer Vision and Machine Learning  eBooks & eLearning

Posted by Underaglassmoon at Nov. 28, 2017
Covariances in Computer Vision and Machine Learning

Covariances in Computer Vision and Machine Learning
Morgan & Claypool | English | 2018 | ISBN-10: 1681730138 | 170 pages | PDF | 2.20 mb

by Minh Ha Quang (Author)
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods

Gerasimos G. Rigatos, "State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods"
English | 2017 | ISBN-10: 3319528653 | 310 pages | EPUB | 6 MB

AI and Machine Learning for Healthcare  eBooks & eLearning

Posted by naag at Nov. 14, 2017
AI and Machine Learning for Healthcare

AI and Machine Learning for Healthcare
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 2 Hours | 5.99 GB
Genre: eLearning | Language: English

Fundamentals of Statistical Modeling and Machine Learning Techniques  eBooks & eLearning

Posted by naag at Oct. 31, 2017
Fundamentals of Statistical Modeling and Machine Learning Techniques

Fundamentals of Statistical Modeling and Machine Learning Techniques
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours | 386 MB
Genre: eLearning | Language: English

Augmented Cognition. Neurocognition and Machine Learning, Part I  eBooks & eLearning

Posted by Jeembo at Oct. 25, 2017
Augmented Cognition. Neurocognition and Machine Learning, Part I

Augmented Cognition. Neurocognition and Machine Learning: 11th International Conference, AC 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I by Dylan D. Schmorrow, Cali M. Fidopiastis
English | 2017 | ISBN: 3319586270 | 586 Pages | PDF | 105.5 MB

This volume constitutes the proceedings of the 11th International Conference on Augmented Cognition, AC 2017, held as part of the International Conference on Human-Computer Interaction, HCII 2017, which took place in Vancouver, BC, Canada, in July 2017.

Advances in Soft Computing and Machine Learning in Image Processing  eBooks & eLearning

Posted by AvaxGenius at Oct. 15, 2017
Advances in Soft Computing and Machine Learning in Image Processing

Advances in Soft Computing and Machine Learning in Image Processing By Aboul Ella Hassanien, Diego Alberto Oliva
English | PDF,EPUB | 2017 (2018 Edition) | 711 Pages | ISBN : 3319637533 | 41.23 MB

This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing.

Kernel Methods and Machine Learning (repost)  eBooks & eLearning

Posted by interes at Oct. 12, 2017
Kernel Methods and Machine Learning (repost)

Kernel Methods and Machine Learning by S. Y. Kung
English | 2014 | ISBN: 110702496X | 572 pages | PDF | 3,5 MB

"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.