• RRP: £53.95
  • You Save: £18.88 (35%)
FREE Delivery in the UK.
Only 7 left in stock (more on the way).
Dispatched from and sold by Amazon. Gift-wrap available.
Quantity:1
Foundations of Machine Le... has been added to your Basket
+ £2.80 UK delivery
Used: Good | Details
Condition: Used: Good
Comment: Dispatched from the US -- Expect delivery in 2-3 weeks. Shows some signs of wear, and may have some markings on the inside. 100% Money Back Guarantee. Shipped to over one million happy customers. Your purchase benefits world literacy!
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See all 2 images

Foundations of Machine Learning (Adaptive Computation and Machine Learning Series) Hardcover – 7 Sep 2012

2 out of 5 stars 1 customer review

See all formats and editions Hide other formats and editions
Amazon Price
New from Used from
Kindle Edition
"Please retry"
Hardcover
"Please retry"
£35.07
£35.07 £30.26
Note: This item is eligible for click and collect. Details
Pick up your parcel at a time and place that suits you.
  • Choose from over 13,000 locations across the UK
  • Prime members get unlimited deliveries at no additional cost
How to order to an Amazon Pickup Location?
  1. Find your preferred location and add it to your address book
  2. Dispatch to this address when you check out
Learn more
£35.07 FREE Delivery in the UK. Only 7 left in stock (more on the way). Dispatched from and sold by Amazon. Gift-wrap available.

Frequently Bought Together

  • Foundations of Machine Learning (Adaptive Computation and Machine Learning Series)
  • +
  • Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series)
  • +
  • Pattern Recognition and Machine Learning (Information Science and Statistics)
Total price: £140.65
Buy the selected items together

Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.

  • Apple
  • Android
  • Windows Phone

To get the free app, enter your e-mail address or mobile phone number.



Product details

  • Hardcover: 480 pages
  • Publisher: MIT Press (7 Sept. 2012)
  • Language: English
  • ISBN-10: 026201825X
  • ISBN-13: 978-0262018258
  • Product Dimensions: 17.8 x 2.1 x 22.9 cm
  • Average Customer Review: 2.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 246,137 in Books (See Top 100 in Books)

Product Description

Review

In my opinion, the content of the book is outstanding in terms of clarity of discourse and the variety of well-selected examples and exercises. The enlightening comments provided by the author at the end of each chapter and the suggestions for further reading are also important features of the book. The concepts and methods are presented in a very clear and accessible way and the illustrative examples contribute substantially to facilitating the understanding of the overall work. Computing Reviews

About the Author

Mehryar Mohri is Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences and a Research Consultant at Google Research. Afshin Rostamizadeh is a Research Scientist at Google Research. Ameet Talwalkar is a National Science Foundation Postdoctoral Fellow in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley.


Inside This Book

(Learn More)
Browse Sample Pages
Front Cover | Copyright | Table of Contents | Excerpt | Index
Search inside this book:

Customer Reviews

2.0 out of 5 stars
5 star
0
4 star
0
3 star
0
2 star
1
1 star
0
See the customer review
Share your thoughts with other customers

Top Customer Reviews

Format: Hardcover
If you're trying to learn about the field by yourself, this is definitely not the book to buy. The authors take a "maths-first" approach to the subject to the point where the overall picture is frequently lost. This rigorous mathematical approach would be OK if the authors actually bothered to fully explain the functions such as what the different variables represent, but they frequently seem to assume for some reason that you already know something about it. The tone of the writing is at times presumptuous, using phrases such as "it is therefore obvious" or some equivalent. It was only when I read an equivalent wikipedia explanation that it became very apparent that their explanations and descriptions of functions left a lot to be desired. The authors seem to avoid at all times stating the obvious and its a real shame because if they were just willing to start with the simple explanation and then delve into the details, this might have been a good book.
Comment 3 of 4 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse

Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: HASH(0x8f957bdc) out of 5 stars 8 reviews
30 of 33 people found the following review helpful
HASH(0x8f969834) out of 5 stars Extremely clear introduction to basic modern theory 3 Oct. 2012
By John Myles White - Published on Amazon.com
Format: Kindle Edition
I picked up this book soon after it came out and found it a wonderful read. Consistent with being a new release, it's more modern than the previous classic ML textbook by Bishop and treats newer subjects that got short shrift there, including PAC learning, VC dimension and Rademacher complexity. It's very well written and does a great job of covering the material that a new student needs to absorb in order to keep up with the current literature in ML. Highly recommended.
12 of 14 people found the following review helpful
HASH(0x8f969888) out of 5 stars Outstanding modern textbook for machine learning 23 Mar. 2014
By Francis Bach - Published on Amazon.com
Format: Hardcover
Some textbooks such as those of Chris Bishop and Kevin Murphy present machine learning from the Bayesian perspective, which is a particular point of view.

In contrast, this book gives an unbiased presentation of machine learning with solid theoretical justifications. It discusses the principles behind the design of learning algorithms by introducing and using the most modern tools and concepts in learning theory. This helps answering many fundamental questions.

The presentation is concise and the topics covered very broad. They include the presentation of several of the most well known binary classification algorithms, multi-class classification, regression, ranking, on-line learning, reinforcement learning, structured prediction, learning theory, and many other topics. In particular, there is a nice and concise presentation of SVMs and boosting. The appendix introduces all the main tools needed, including a brief introduction to convex optimization.

I strongly recommend this book to students and researchers. It gives a very modern presentation covering all the main topics in learning, which can serve as a reference for everyone. Perhaps more importantly, it helps us analyze and understand machine learning.
4 of 4 people found the following review helpful
HASH(0x8f969cc0) out of 5 stars Excellent book that everyone should learn from 23 Jun. 2015
By MR Brain - Published on Amazon.com
Format: Hardcover Verified Purchase
The best book on machine learning theory. This book is extremely clear and is a must-have for any serious machine learning or statistical learning scholar. As the title suggests, this book builds the foundations of machine learning, which are omitted in every other machine learning text book that I've read. This book will prepare you for advanced, research level machine learning papers. There is no other book like it - absolutely incredible! This is the book that experts and professors in the field learn from. Even if you have 10+ years of experience in the field, I'm sure that you will learn something new every time you pick up the book. Furthermore, the book is concise enough that even an beginner could learn from it. Although any beginner should be prepared to read more on their own. A basic understanding of probability theory, linear algebra, and optimization is assumed - although the appendix has the clearest survey of linear algebra, basic probability, and basic optimization that I've ever read. Seriously - this book is incredible.
10 of 16 people found the following review helpful
HASH(0x8f969ca8) out of 5 stars Do not buy the Kindle Version... its unreadable 3 Mar. 2015
By Tyler Hill - Published on Amazon.com
Format: Kindle Edition Verified Purchase
I wish I could give 0 stars. This "kindle book" is completely unreadable. Sadly, the authors decided they could make a PDF version of the book, charge $40 and still call it a Kindle Book. Kindle books are legible on the mobile kindle apps. This book is not. Amazon shouldn't let them sell it as I just wasted $40 on something I can't even use. Now I must buy the paper version...
6 of 10 people found the following review helpful
HASH(0x8f96c090) out of 5 stars Excellent book for undergraduates 15 Dec. 2013
By Keyulu - Published on Amazon.com
Format: Hardcover Verified Purchase
Excellent book. Used for my second year undergraduate learning theory course. Very we'll written. Recommend this for all CS undergraduates who are interested in learning theory.
Were these reviews helpful? Let us know


Feedback