menu
{ "item_title" : "Machine Learning", "item_author" : [" Peter Flach "], "item_description" : "As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/10/742/222/1107422221_b.jpg", "price_data" : { "retail_price" : "70.00", "online_price" : "70.00", "our_price" : "70.00", "club_price" : "70.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Learning|Peter Flach

Machine Learning : The Art and Science of Algorithms That Make Sense of Data

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

This item is Non-Returnable

Details

  • ISBN-13: 9781107422223
  • ISBN-10: 1107422221
  • Publisher: Cambridge University Press
  • Publish Date: September 2012
  • Dimensions: 9.6 x 7.3 x 0.9 inches
  • Shipping Weight: 2.25 pounds
  • Page Count: 409

Related Categories

You May Also Like...

    1

BAM Customer Reviews