Machine learning - a probabilistic perspective
نویسنده
چکیده
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Machine learning : a probabilistic perspective / Kevin P. Murphy. p. cm. — (Adaptive computation and machine learning series) Includes bibliographical references and index. Contents Preface xxvii 1 Introduction 1 1.1 Machine learning: what and why? 1 1.1.1 Types of machine learning 2 1.2 Supervised learning 3 1.2.1 Classification 3 1.2.2 Regression 8 1.3 Unsupervised learning 9 1.3.1 Discovering clusters 10 1.3.2 Discovering latent factors 11 1.3.3 Discovering graph structure 13 1.3.4 Matrix completion 14 1.4 Some basic concepts in machine learning 16 1.4.1 Parametric vs non-parametric models 16 1.4.2 A simple non-parametric classifier: K-nearest neighbors 16 1.4.3 The curse of dimensionality 18 1.4.4 Parametric models for classification and regression 19 1.4.5
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