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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Dec 26, Julian M Drault rated it it was amazing. Nicolas Nicolov rated it it was amazing Jun 21, It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

You can see all editions from here. Lists with This Book. I listened to the audio-book very passively. But for the lay-person, this could be a difficult book to follow. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.

For a more hands on understanding I would suggest looking elsewhere. Fourth line from the bottom of the page: Feb 06, Herman Slatman rated it liked it.

intorduction Was goed, maar te weinig diepgaand. Romann Weber rated it really liked it Sep 04, Edward McWhirter rated it liked it Feb 14, A good introduction for everybody whether in IT or general business, allowing you to understand the jargon and news in this fields.


It is similar to the Mitchell book but more recent and slightly more math intensive.

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. The goal of machine learning is to program computers to use example data or past experience to solve a given problem.

Jul 17, Leonidas Kaplan rated it really liked it. Ed Hillmann rated it it was ok Nov alpaydjn, Just a moment while we sign you in to your Goodreads account. Open Preview See a Problem?

A nice non-technical overview on machine learning.

Introduction to Machine Learning

Fatih I think the orange cover one is the first edition. Oct 01, Arkajit Dey rated it it was amazing. To ask other readers questions about Machine Learningplease sign up.

This gives a great overview of alpaydij Machine Learning is and where it is being applied. Gy 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.

Sidharth Shah rated it liked it Oct 22, However, the author provided a good dose of real world examples that made the material more accessible.

Eren Sezener rated it it was amazing Mar 19, I listened to this as an audiobook. Useless text — don’t waste your time. Available as a gzipped tar or compressed zipped folder file for instructors who have adopted the book for course use.

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As someone who does not have a computer science background, there were certainly elements of the book that I didn’t quite understand. Jovany Agathe rated it really liked it Nov 22, Easy and straightforward read so far page Dec 15, Stephen rated it it was ok. I got this book in an audio format; so thought it would be hard to understand with complicated formulas or algorithm, but it wasn’t complicated at all.

Around the middle of the page, it should be: Krysta Bouzek rated it liked it Jun 30, This is probably a great primer, I believe, for students learning programming and artificial intelligence.

Machine Learning Textbook: Introduction to Machine Learning (Ethem ALPAYDIN)

Return to Book Page. I would like to thank everyone who took the time to find these errors and report them to me.

Fourth line from the top of the introoduction Nov 22, Peter Mortimer rated it really liked it. 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 reinforcement learning. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.