INTRODUCTION TO MACHINE LEARNING, FOURTH EDITION Hardcover

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Explore the world of artificial intelligence with "INTRODUCTION TO MACHINE LEARNING, FOURTH EDITION Hardcover," a comprehensive guide by Ethem Alpaydin. Published by MIT Press in 2020, this text delves into the fundamentals of machine learning, suited for readers with a background in computer engineering and those interested in the applications of AI in computing. The hardcover edition is part of the Adaptive Computation and Machine Learning Series and is penned in clear, accessible English. It measures 9.3 inches in length, 8.3 inches in width, and has a depth of 1.5 inches, with a substantial weight of 51.1 ounces for a sturdy and substantial reading experience. Whether for study or reference, this textbook is an essential addition to any academic library.

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1987 Fundamentals of Mathematics: Volume II Geometry By MIT Press 4th Printing

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The 1987 Fundamentals of Mathematics: Volume II Geometry by MIT Press is a comprehensive textbook on geometry suitable for adult and further education levels. Authored by F. Bachmann, this hardcover book features 696 pages filled with illustrations to help students understand the concepts better. This 4th printing edition is a valuable resource for those looking to delve deeper into the subject area of mathematics, specifically focusing on geometry in a clear and informative manner. Smoke and pet free household. We combine shipping on multiple orders!!!

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Introduction to Machine Learning, fourth edition Hardcover FREE SHIP

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The "Introduction to Machine Learning, fourth edition Hardcover" is a comprehensive textbook published by MIT Press in 2020. Authored by Ethem Alpaydin, this book is part of the Adaptive Computation and Machine Learning Series. With a focus on the subject area of Computers, particularly in the fields of Intelligence (AI) & Semantics and Computer Engineering, this textbook provides a comprehensive overview of machine learning concepts suitable for both classroom learning and self-study. The hardcover format and large size make it a substantial and durable resource for readers interested in delving into the complexities of machine learning.

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