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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Foundations of Machine Learning (Hardcover)
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Foundations of Machine Learning (Hardcover)
Series: Adaptive Computation and Machine Learning series
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Fundamental topics in machine learning are presented along with
theoretical and conceptual tools for the discussion and proof of
algorithms. This graduate-level textbook introduces fundamental
concepts and methods in machine learning. It describes several
important modern algorithms, provides the theoretical underpinnings
of these algorithms, and illustrates key aspects for their
application. The authors aim to present novel theoretical tools and
concepts while giving concise proofs even for relatively advanced
topics. Foundations of Machine Learning fills the need for a
general textbook that also offers theoretical details and an
emphasis on proofs. Certain topics that are often treated with
insufficient attention are discussed in more detail here; for
example, entire chapters are devoted to regression, multi-class
classification, and ranking. The first three chapters lay the
theoretical foundation for what follows, but each remaining chapter
is mostly self-contained. The appendix offers a concise probability
review, a short introduction to convex optimization, tools for
concentration bounds, and several basic properties of matrices and
norms used in the book. The book is intended for graduate students
and researchers in machine learning, statistics, and related areas;
it can be used either as a textbook or as a reference text for a
research seminar.
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