Genetic Algorithms in Search, Optimisation And Machine Learning

Mathematical Methodologies in Pattern Recognition and Machine Learning [Repost]

Pedro Latorre Carmona, J. Salvador Sánchez, Ana L.N. Fred - Mathematical Methodologies in Pattern Recognition and Machine Learning
Published: 2012-11-10 | ISBN: 1461450756 | PDF | 202 pages | 3 MB
Mathematical Methodologies in Pattern Recognition and Machine Learning

Pedro Latorre Carmona, "Mathematical Methodologies in Pattern Recognition and Machine Learning: Contributions from the International Conference on Pattern Recognition … Proceedings in Mathematics & Statistics) "
ISBN: 1461450756 | 2012 | PDF | 200 pages | 5.1 MB
Privacy and Security Issues in Data Mining and Machine Learning: International

Christos Dimitrakakis, Aris Gkoulalas-Divanis, Aikaterini Mitrokotsa, Vassilios S. Verykios, Yücel Saygin, "Privacy and Security Issues in Data Mining and Machine Learning: International ECML/PKDD Workshop, PSDML 2010, Barcelona, Spain, September 24, 2010. … / Lecture Notes in Artificial Intelligence)"
Publisher: Springer | ISBN 10: 3642198953 | 2011 | PDF | 149 pages | 2 MB
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics by Justin Solomon
English | 2015 | ISBN: 1482251884 | 392 pages | PDF | 10 MB

Applied Genetic Programming and Machine Learning (Repost)  

Posted by Specialselection at March 18, 2013
Applied Genetic Programming and Machine Learning (Repost)

Hitoshi Iba, Yoshihiko Hasegawa, Topon Kumar Paul, "Applied Genetic Programming and Machine Learning"
English | 2009-08-26 | ISBN: 1439803692 | 338 pages | PDF | 4.3 mb
Learning Kernel Classifiers: Theory and Algorithms  (Adaptive Computation and Machine Learning)

Ralf Herbrich, "Learning Kernel Classifiers: Theory and Algorithms"
The M.I.T Press | 2001 | ISBN: 026208306X | 384 pages | PDF | 2,4 MB

Applied Genetic Programming and Machine Learning  

Posted by tot167 at March 30, 2010
Applied Genetic Programming and Machine Learning

Hitoshi Iba, Yoshihiko Hasegawa, Topon Kumar Paul, "Applied Genetic Programming and Machine Learning"
CRC Press | 2009 | ISBN: 1439803692 | 349 pages | PDF | 10,9 MB

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.
Principles of Data Mining (Adaptive Computation and Machine Learning) by  David J. Hand

Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand, Heikki Mannila, Padhraic Smyth
Publisher: The MIT Press (August 1, 2001) | ISBN-10: 026208290X | PDF | 30,6 Mb | 425 pages

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.
A collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (II)

Dr Antonio Gulli , "A collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (II): Hands-on Big Data and Machine … of Programming Interview Questions"
2015 | ISBN-10: 1518678645 | 106 pages | Djvu | 1 MB