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.
|Published (Last):||12 October 2009|
|PDF File Size:||17.4 Mb|
|ePub File Size:||19.25 Mb|
|Price:||Free* [*Free Regsitration Required]|
There are no discussion topics on this book yet. In this sense, it can be leqrning quick read and good overview – and enough discussion surrounding the derivations so that they are fairly easy to follow.
Jan 05, Brian Baquiran rated it liked it Shelves: Just a moment while we sign you in to your Goodreads account. Apr 23, Leonardo marked it as to-read-in-part Shelves: Eren Sezener rated it it was amazing Mar 19, As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning — the foundation of efforts to process that data into knowledge — has also advanced.
Teresa Tse rated it it was ok Jul 09, Useless text — don’t waste your time. See 2 questions about Introduction to Machine Learning…. Dec 02, Abe Shocket rated it it was ok. If you are after learning about the algorithms or specifics of how machine learning works, you will likely be disappointed which, admittedly, was my reaction because of my expectations and goals.
This is probably a great primer, I believe, for students learning programming and artificial intelligence. Want to Read saving….
There will be a wide reaction to this based on the reader’s expectations. Feb 16, Castemelijn rated it really liked it. I listened to the audio-book very passively. Even so, by understanding the conceptual parts of machine learning, I believe many will have an intuitive idea about what can be in the making.
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. It is similar to the Mitchell book eghem more recent and slightly more math intensive. The book can be used by advanced undergraduates and graduate students who have alpaydi courses in computer programming, probability, calculus, and linear algebra.
Kindle Editionpages. 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.
Just a moment while we sign you in to your Goodreads account.
Machine Learning by Ethem Alpaydin
Want to Read Currently Reading Read. Little bit hard to get through, but otherwise quite good as an introductory book. See Mitchell, ; Russell and Norvig; All chapters have been revised and updated. Mar 12, Nick Hargreaves rated it really liked it.
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.
Oct 09, Scott rated it it was amazing. Want to Read saving…. I will macuine happy to be told of others. Thanks for telling us about the problem.
inhroduction The goal of machine learning is to program computers to use example data or past experience to solve a given problem. 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.
Introduction to Machine Learning
Jan 26, Juan Carlos rated it really liked it. It is official page of author on university website. If you want to actually start using machine learning, you’ll need a more comprehensive book, of course.
No math or code, but manages to convey the basic ideas behind fundamental ML algorithms from linear regressions to neural networks.
Really knew all this topics, but the book helped me arrange some concepts I had mixed up a bit.