menu
{ "item_title" : "A Footbridge to Machine Learning", "item_author" : [" Stefan Janaqi "], "item_description" : "This introductory text builds a bridge between mathematical foundations and machine learning algorithms. Assuming only a basic background in mathematics, statistics, and scientific computing, it develops the key concepts underlying machine learning through three fundamental questions: Which samples are important? Which features are important? How can we generate new samples? Topics include support vector machines, sparse learning, optimization, information theory, and generative models.Emphasizing mathematical understanding rather than black-box use of software, the book is suitable for advanced undergraduate students, beginning graduate students, and practitioners seeking a rigorous introduction to machine learning.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/03/244/920/3032449200_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
A Footbridge to Machine Learning|Stefan Janaqi

A Footbridge to Machine Learning : A 12-Week Introductory Course

PRE-ORDER NOW:
local_shippingShip to Me
Preorder. This item will be available on January 22, 2027 .
FREE Shipping for Club Members help

Overview

This introductory text builds a bridge between mathematical foundations and machine learning algorithms. Assuming only a basic background in mathematics, statistics, and scientific computing, it develops the key concepts underlying machine learning through three fundamental questions: Which samples are important? Which features are important? How can we generate new samples? Topics include support vector machines, sparse learning, optimization, information theory, and generative models.

Emphasizing mathematical understanding rather than black-box use of software, the book is suitable for advanced undergraduate students, beginning graduate students, and practitioners seeking a rigorous introduction to machine learning.

This item is Non-Returnable

Details

  • ISBN-13: 9783032449207
  • ISBN-10: 3032449200
  • Publisher: Springer
  • Publish Date: January 2027

Related Categories

You May Also Like...

    1

BAM Customer Reviews