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Synopsis
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About the Author
Ethem Alpaydin is Professor in the Department of Computer Engineering at Özyegin University and Member of The Science Academy, Istanbul. He is the author of <I>Machine Learning: The New AI</I>, a volume in the MIT Press Essential Knowledge series.s).
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- PublisherMIT Press
- Publication date2020
- ISBN 10 0262043793
- ISBN 13 9780262043793
- BindingHardcover
- LanguageEnglish
- Edition number4
- Number of pages712
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Introduction to Machine Learning, fourth edition (Adaptive Computation and Machine Learning series)
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The MIT Press (edition 4), 2020
ISBN 10: 0262043793
ISBN 13: 9780262043793
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Introduction to Machine Learning, fourth edition (Adaptive Computation and Machine Learning series)
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hardcover. Condition: Very Good. 4. Very Good Condition and Unread! Text is clean and unmarked! Light shelf wear to cover from storage. Has a small black line or red dot on bottom/exterior edge of pages. Seller Inventory # MIT-HC-VG-0262043793
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INTRODUCTION TO MACHINE LEARNING 4E
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Introduction to Machine Learning (Adaptive Computation and Machine Learning series)
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Hardcover. Condition: Very Good. Dust Jacket Condition: Very Good. 4th Edition. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks. The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals. Seller Inventory # ABE-1727021002585
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Introduction to Machine Learning (Adaptive Computation and Machine Learning series)
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Introduction to Machine Learning
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Introduction to Machine Learning
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Introduction to Machine Learning
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Introduction To Machine Learning, Fourth Edition
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Introduction to Machine Learning (Adaptive Computation and Machine Learning series)
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