Introduction to Machine Learning (Adaptive Computation and Machine Learning series) [Ethem Alpaydin] on *FREE* shipping on qualifying offers. Introduction To Machine Learning 3Rd Edition [Ethem Alpaydin] on *FREE* shipping on qualifying offers. Paperback International Edition Same. Introduction to Machine Learning is a comprehensive textbook on the subject, Ethem Alpaydin, Design and Analysis of Classifier Learning Experiments in.
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The clarity of the writing, as well as the excellent structure and scope, impressed me. I am more than pleased to find that this second edition continues to be learnin informative and comprehensive, as well as easy to read and follow.
Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support? The goal of machine learning is to program computers lesrning use example data or past experience to solve a given problem.
Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.
The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts.
In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topi.
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Introduction to Machine Learning
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Page 1 of 1 Start over Page 1 of 1. Customers who bought this item also bought. Review “A few years ago, I used the first edition of this book as a reference book for a project I was working on. Adaptive Computation and Machine Learning series Hardcover: Try the Kindle edition and experience these great reading features: Share your thoughts with other customers.
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May alpaaydin be useful for practitioners. If you would like to have a decent Machine Learning Text for reference – this book may suffice. Machije your interest is in learning t techniques and apply them to real life examples – this book will most likely not meet the expectation. This is an excellent intro to machine learning. It is well illustrated and takes you through the essential concepts like linear classifiers, kernels, Bayesian inference, etc.
One person found this helpful. Kindle Edition Verified Purchase. It’s so bad it gives Kindle a bad name, and shouldn’t be allowed to be sold here. The diagrams are scaled improperly: I hope one day to meet Alpaydin in person to thank him profusely for this book and earlier versions. He unpacks the major concepts of machine learning introeuction a manner that makes it very easy to follow. I probably have 3 copies of the earlier edition. I bought this one, and am very pleased with the updates alpaaydin specifically related to neural networks and deep learning.
If you’re running around in this domain – this book is crucial. This was recommended to me by a colleague. Reading through it has sharpened my understanding in the areas of machine learning that I already knew, and broadened it into the areas that I’ve only heard colloquially.
I would recommend this to anyone aspiring to get a grounding in this broad field. The topics and concepts in this book are exceptionally well organized. After reading it from cover to cover, I could easily see how all macbine ideas and concepts fit into place. I have two main criticisms. First, the notation is sometimes non-standard, e.
Second, the explanations are sometimes too brief.
Introduction to Machine Learning by Ethem Alpaydin
For example, when deriving the solution for Least Squares Regression with Quadratic Discriminants, Vandermode matrices are used but the author fails to identify them as such, or to explain why they are useful. If the author were to write an extra sentence on every other page, the explanations would be perfect! I had a bachelor degree in computer science and ihtroduction I am a student transportation. The textbooks in transportation used a lot of statistic theory, but none of them wrote in a understandable way.
Just from the table of content, you will find out maximum likelihood, linear discriminant analysis and principal component analysis, etc. All of them machinne in a comprehensible way. It’s a good book for those who studied in CS and now want to learn statistic by yourself to process and classify huge amount of data.
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