menu
{ "item_title" : "Mathematics of Machine Learning", "item_author" : [" Ming Lei "], "item_description" : "This book aims to equip readers with the essential mathematical foundation for machine learning, deep learning, and reinforcement learning. A strong grasp of mathematics is crucial for understanding the underlying principles of these fields. The text systematically introduces key topics such as calculus, linear algebra, probability theory, optimization methods, information theory, stochastic processes, and graph theory--core mathematical concepts essential for mastering machine learning. The material is presented concisely, covering the necessary mathematics with precision and clarity. Through comprehensive explanations and their applications in machine learning, the practical significance of these mathematical tools is clearly demonstrated. By the end of the book, readers will have built a solid mathematical foundation, preparing them for advanced academic research and product development. A prerequisite for this book is a prior study of undergraduate-level calculus, linear algebra, and probability theory. ", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/81/925/568/9819255686_b.jpg", "price_data" : { "retail_price" : "84.99", "online_price" : "84.99", "our_price" : "84.99", "club_price" : "84.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Mathematics of Machine Learning|Ming Lei

Mathematics of Machine Learning : A Handbook

PRE-ORDER NOW:
local_shippingShip to Me
Preorder. This item will be available on March 13, 2027 .
FREE Shipping for Club Members help

Overview

This book aims to equip readers with the essential mathematical foundation for machine learning, deep learning, and reinforcement learning. A strong grasp of mathematics is crucial for understanding the underlying principles of these fields. The text systematically introduces key topics such as calculus, linear algebra, probability theory, optimization methods, information theory, stochastic processes, and graph theory--core mathematical concepts essential for mastering machine learning. The material is presented concisely, covering the necessary mathematics with precision and clarity. Through comprehensive explanations and their applications in machine learning, the practical significance of these mathematical tools is clearly demonstrated. By the end of the book, readers will have built a solid mathematical foundation, preparing them for advanced academic research and product development. A prerequisite for this book is a prior study of undergraduate-level calculus, linear algebra, and probability theory.

This item is Non-Returnable

Details

  • ISBN-13: 9789819255689
  • ISBN-10: 9819255686
  • Publisher: Springer
  • Publish Date: March 2027
  • Page Count: 552

Related Categories

You May Also Like...

    1

BAM Customer Reviews