An Introduction to Machine Learning by Miroslav Kubat (2017, Hardcover) Be the first to write a review. Miroslav Kubat (auth.) An Introduction to Machine Learning by Miroslav Kubat, Aug 18, 2018, Springer edition, paperback I wanted to learn about machine learning, so I started to read this book. Classifiers. Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. È un manuale a 360° che consiglierei a chiunque voglia avvicinarsi all'argomento. Three years later, a follow-up book appeared and then another. . Course … An Introduction to Machine Learning. It covers all the main algorithms and practical challenges of building complex models. This book provides an excellent combination of theory and practice that is easy to follow and understand. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. 1 review. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work. One chapter is dedicated to the popular genetic algorithms. Please try again. Use the Amazon App to scan ISBNs and compare prices. $44.99; $44.99; Publisher Description. Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. Springer; Softcover reprint of the original 2nd ed. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. Unformatted text preview: Miroslav Kubat An Introduction to Machine Learning An Introduction to Machine Learning Miroslav Kubat An Introduction to Machine Learning 123 Miroslav Kubat Department of Electrical and Computer Engineering University of Miami Coral Gables, FL, USA ISBN 978-3-319-20009-5 ISBN 978-3-319-20010-1 (eBook) DOI 10.1007/978-3-319-20010-1 Library of Congress Control … Full content visible, double tap to read brief content. . A friend recommended taking an online Stanford University machine learning course taught by Andrew Ng (available from Coursera) and I find the online course to be far more helpful than this book. The lowest-priced brand-new, unused, unopened, undamaged item in its original packaging (where packaging is applicable). . You might be able to brute-force your way to a program that works, but why waste your time when these are solved problems? . Read "An Introduction to Machine Learning" by Miroslav Kubat available from Rakuten Kobo. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, … This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. Top subscription boxes – right to your door, © 1996-2020, Amazon.com, Inc. or its affiliates. Kubat smoothly teaches Machine Learning in a comprehensive and conceptual way so that you really understand the field fundamentally. Please try again. Starting from some random initial state of a sliding-tile trying to obtain a given final state using hill climbing algorithm. This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free. Non è mai facile scrivere di un argomento tecnico in modo adeguatamente strutturato e chiaro. 2017 by Kubat, Miroslav (ISBN: 9783319639123) from Amazon's Book Store. There was an error retrieving your Wish Lists. This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. Brand new: lowest price. This is a must read for anyone that wants to get into machine learning. Robots will become the value creators and human will become more and more a bunch of useless parasites without meaningful works and not really knowing why they exit on earth at all. There was a problem loading your book clubs. . Contents 1 A Simple Machine-Learning Task ..... 1 1.1 Training Sets and Classifiers.... .

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. 2015 edition (October 15, 2016). . . 1.1 Hill climbing for sliding-tile. It fills a longstanding need for texts that cover the middle ground of neither oversimplifying nor too technical explanations of key concepts of key Machine Learning algorithms. Machine Learning: The AI Approach1 was a thick volume of research papers which proposed the most diverse ways of addressing the great mystery. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. . Inoltre, al termine di ogni capitolo è presente un riassunto, comodo per ripescare rapidamente un concetto di cui si ha necessità sul momento, qualche nota storica e delle proposte di esercizi. Stock photo. The programming exercises offer additional opportunities to get deeper into the implementation aspects of the different algorithms and concepts - very valuable if you really try them. Miroslav Kubat. 34 Full PDFs related to this paper. Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm Reviewed in the United States on April 20, 2020. Please try again. … The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. It also analyzes reviews to verify trustworthiness. Kubat approaches the field of machine learning in a very mathematical yet intuitive way. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work. The 13-digit and 10-digit formats both work. The topics cover many topics both foundational and contemporary. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. It fills a longstanding need for texts that cover the middle ground of neither oversimplifying nor too technical explanations of key concepts of key Machine Learning algorithms. Miroslav Kubat This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. It does not overwhelm you with formulas and theories for you to sit and figure them out, but rather supports the theories with examples. Awful. . This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The print version of this textbook is ISBN: 9783319639130, 3319639137. . Awful. . A perfect combination of theory and application, excellent as a textbook! Reviewed in the United States on October 10, 2016. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. . The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. 46 Scopus citations. After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in. After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in. . You must be able to properly program using a mathematical basis to utilize this book to its fullest potential. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. … This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. Book Description. Everyday low prices and free delivery on eligible orders. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. Miroslav Kubat An Introduction to Machine Learning Second Edition. Introduction. 2017 edition (August 18, 2018). . . To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Under their influence, a new scientific discipline was born—virtually overnight. . . The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, … . . xii Introduction The bombshell exploded in 1983. There was an error retrieving your Wish Lists. . This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. … “Miroslav Kubat's Introduction to Machine Learning is an excellent overview of a broad range of Machine Learning (ML) techniques. Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. 2017 Edition. One chapter is dedicated to the popular genetic algorithms. About this product. . Download. Please try again. Miroslav Kubat's 20 research works with 82 citations and 417 reads, including: An Introduction to Machine Learning. Unsupervised Learning. Reviewed in the United States on July 13, 2020. Brief content visible, double tap to read full content. An Introduction to Machine Learning. It fills a longstanding need for texts that cover the middle ground of neither oversimplifying nor too technical explanations of key concepts of key Machine Learning algorithms. Your recently viewed items and featured recommendations, Select the department you want to search in, Softcover reprint of the original 2nd ed. Excellent Introduction to machine learning - a must have, Reviewed in the United States on November 4, 2016. READ PAPER. Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for more than a quarter century. Awful. If you are going to read one book on machine learning, read this one, Reviewed in the United States on May 21, 2016. Mechanism, implementation, and application of systems that improve automatically based on past experience, including classification, density estimation, clustering, and online learning tasks and solutions. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Unable to add item to List. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. 2015 by Kubat, Miroslav (ISBN: 9783319200095) from Amazon's Book Store. It fills a longstanding need for texts that cover the middle ground of neither oversimplifying nor too technical explanations of key concepts of key Machine Learning algorithms. There's a problem loading this menu right now. Miroslav Kubat This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. . “Miroslav Kubat's Introduction to Machine Learning is an excellent overview of a broad range of Machine Learning (ML) techniques. … this book could serve as textbook for an undergraduate introductory course on machine learning … .” (Gilles Teyssière, Mathematical Reviews, April, 2017). This is a good book to learn the technical aspects of Machine Learning. This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The 13-digit and 10-digit formats both work. Unlike many of other machine learning books, this one takes a very simplistic approach and uses simple but very useful examples in each chapter. The book is wholly unreadable on kindle format. Top subscription boxes – right to your door, © 1996-2020, Amazon.com, Inc. or its affiliates. . Buy An Introduction to Machine Learning 2nd ed. Save up to 80% by choosing the eTextbook option for ISBN: 9783319639130, 3319639137. . Intro To Machine Learning My solutions of the tasks from the book: Miroslav Kubat An Introduction to Machine Learning. Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. There was a problem loading your book clubs. I did learn a lot, but I felt that completing the programming assignments was like reinventing the wheel. A short summary of this paper. Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. 2015 Edition. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. 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