Introduction to Machine Learning (Adaptive Computation and Machine Learning series) [Ethem Alpaydin] on *FREE* shipping on qualifying offers. Introduction to Machine Learning has ratings and 11 reviews. Rrrrrron said: Easy and straightforward read so far (page ). However I have a rounded. I think, this book is a great introduction to machine learning for people who do not have good mathematical or statistical background. Of course, I didn’t.
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Introduction to Machine Learning
Instructors using the book are welcome to use these figures in their lecture slides as long as the use is non-commercial and the source is cited. 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. Goodreads helps you keep track of books you want to read.
Omri Cohen rated it really liked it Sep 05, There will be a wide reaction to this based on the reader’s expectations. Nonetheless, if Machine Learning history, and fundamentals are for you, then I recommend ‘Machine Learning’. Useful as a refresher and quick overview of the field, with pointers to the key papers for further in-depth reading as needed. Ethem does a great job at explaining the big picture through common real-life examples, using relatively standard math.
Refresh and try again. A great read nontheless. Kanwal Hameed rated it it was amazing Mar 16, Index of summation should be Y in the second summation Alex Kogan.
The upside, is that the book is currently very relevant, with its reference to ‘Alpha Go’, which is the artificial intelligence that beat one of the most complex b I listened to the audio-book very passively. In this sense, it can be a quick read and good overview – and enough discussion surrounding the derivations so that they are fairly easy to follow.
There are inntroduction discussion topics on this book yet. Jan 05, Brian Baquiran rated it liked it Shelves: A great overview of Machine Allaydin. All chapters have been revised and updated. Of course, I didn’t understand all the concepts mentioned, but whatever I understood, I enjoyed it.
All learning algorithms are explained etjem that the student can easily move from the equations in the book to a computer program. Krysta Bouzek rated it liked it Jun 30, This was a short book and I did not enjoy it.
Very good for starting. Hardly qualify Essential Knowledge, better macjine read Wikipedia. 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.
It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions.
Machine Learning by Ethem Alpaydin
The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and ethe learning. Kaiser rated it liked it Dec 26, This gives a great overview of what Machine Learning is and where it is being applied.
Larning it is a good statement of the types of problem we like to solve, with intuitive examples, and the character of the solutions that classes of techniques will yield. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web with downloadable results for instructors ; and many additional exercises.
A decent high-level overview of machine learning, for non-technical types. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. If your expectations are right, you’ll like it, because the author clearly knows a lot, but it wasn’t the “give me a methodical overview” that I was wanting.
The very last eq on the bottom of the page; the prob is 0. Books by Ethem Alpaydin. Open Preview See a Problem?
But once that part has past, the author Alpaydin explains the conceptual ideas behind the algorithms and the thinking surro Summary: Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition — as well as some we don’t yet use everyday, including driverless cars.
Alexander Matyasko rated alpaydi really liked it May 02, Mar 12, Nick Hargreaves rated it really liked it.
Introduction to Machine Learning by Ethem Alpaydin
Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.
Created on Oct 24, by E. For instructors to use in their courses; please machibe the first page and footer if alpayydin edit the slides. Overall, if you want to understand and introduction to machine introducton and how it works, this book will do the job. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, ti faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.
It is similar to the Mitchell book but more recent and slightly more math intensive.