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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This review has been hidden because it contains spoilers. It will also be of interest to engineers in the field who alpaydkn concerned with the application of machine learning methods. There will be a wide reaction to this based on the reader’s expectations. I’m torn on my reaction to this.
The book great insights about what is machine learning, how are were using it, ways to enforce learning in machine and as a whole what impact it will create in our lives. I would highly recommend this book if you like to conceptually understand the different topics and models of Machine Learning as it exists today. Rrrrrron rated it really liked it Apr 07, I would like to thank everyone who took the time to find these errors and report them to me.
Was goed, maar te weinig diepgaand.
This book, oddly, starts by explaining the absolutely most trivial things about technology and the Internet — e. But of course, for the doers, going to fx. If you want to actually start using machine learning, you’ll need a more comprehensive book, of course.
The following lecture slides pdf and ppt are made available for instructors using the book. 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.
Really knew all this topics, but the book helped me arrange some concepts I had mixed up a bit. Two lines below Eq. Romann Weber rated it really liked it Sep 04, It gives a very broad overview of the different etbem and methodologies available in the ML field.
Clearly written and clearly thought out, but shallow for anyone already familiar with the field.
Machine Learning Textbook: Introduction to Machine Learning (Ethem ALPAYDIN)
Introduction to Machine Learning by Ethem Alpaydin. After an introduction that defines machine learning and gives examples of machine learning applications, the machind covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Itnroduction models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.
Teresa Tse rated it it was ok Jul 09, A nice non-technical overview on machine learning. Thanks for telling us about the problem. But once that part has past, the author Alpaydin explains the conceptual ideas behind the algorithms and the thinking surro Summary: This gives a great overview of what Machine Learning is and where it is being applied.
Iva Miholic rated it it was amazing Jul 27, All chapters have been revised and updated. Fourth line from the top introduvtion the page: Alpaydin does this without ever becoming really technical, and this book is for understanding the basic concepts, not the doing. 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 board games.
He was appointed Introducion Professor in and Professor in in the same department. Created on Feb 11, by E.
Introduction to Machine Learning
To ask other readers questions about Machine Learningplease sign up. However, the author provided a good dose of real world examples that made the material more accessible. Just a moment while we sign you in to your Goodreads account. I listened to the audio-book very passively.
Huwenbo Shi rated it liked it Apr 03, Mar 12, Nick Hargreaves rated it really liked it. Bharat Gera rated it it was amazing Jan 02, To see what your friends thought of this book, please sign up. So 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 alpayvin yield.
You can see all editions from here. Jan 05, Brian Baquiran rated it liked it Shelves: Useful as a refresher and quick overview of the field, with pointers to the key papers for further in-depth reading as needed. It is official page of author on university website. Very decent introductory book. Useless text — don’t waste your time. Roberto Salgado rated it really liked it Aug 01, Hardcoverpages.
Jan 26, Juan Carlos rated it really liked it.
If you just want an overview focused more on uses, history and where it may go, with only a little dipping into specifics, you will likely greatly appreciate this. Each chapter reads almost independently. 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, a The goal of machine learning is to program computers to use example data or past experience to solve a given problem.
Sep 11, Miroslav Pikus rated it really liked it.