[DOWNLOAD^^][PDF] Understanding Machine Learning From Theory to Algorithms [PDF EBOOK EPUB]

[DOWNLOAD^^][PDF] Understanding Machine Learning: From Theory to Algorithms
[PDF EBOOK EPUB]
Understanding Machine Learning: From
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[DOWNLOAD^^][PDF] Understanding Machine Learning: From Theory to Algorithms
[PDF EBOOK EPUB]

[DOWNLOAD^^][PDF] Understanding Machine Learning: From Theory to Algorithms [PDF
EBOOK EPUB]
[DOWNLOAD^^][PDF]
Understanding
Machine Learning:
From Theory to
Algorithms [PDF
EBOOK EPUB]
Description
'This elegant book covers both rigorous theory and practical methods of machine learning. This
makes it a rather unique resource, ideal for all those who want to understand how to find structure
in data.' Bernhard SchÃlkopf, Max Planck Institute for Intelligent Systems'This is a timely text on
the mathematical foundations of machine learning, providing a treatment that is both deep and
broad, not only rigorous but also with intuition and insight. It presents a wide range of classic,
fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This
is a great book for anyone interested in the mathematical and computational underpinnings of this
important and fascinating field.' Avrim Blum, Carnegie Mellon University'This text gives a clear and
broadly accessible view of the most important ideas in the area of full information decision
problems. Written by two key contributors to the theoretical foundations in this area, it covers the
range from theoretical foundations to algorithms, at a level appropriate for an advanced
undergraduate course.' Peter L. Bartlett, University of California, Berkeley Read more Machine
learning makes use of computer programs to discover meaningful patters in complex data. It is
one of the fastest growing areas of computer science, with far-reaching applications. This book
explains the principles behind the automated learning approach and the considerations underlying
its usage. The authors explain the 'hows' and 'whys' of the most important machine-learning
algorithms, as well as their inherent strengths and weaknesses, making the field accessible to
students and practitioners in computer science, statistics, and engineering. Read more See all
Editorial Reviews