Target New ArrivalsGift Ideas for DadFourth of JulyClothing, Shoes & AccessoriesHome & DecorKitchen & DiningOutdoor Living & GardenGroceryHousehold EssentialsBabyBeautyPersonal CareSports & OutdoorsHealthWellnessLuggageSchool & Office SuppliesToys & GamesElectronicsVideo GamesMovies, Music & BooksParty SuppliesGift IdeasGift CardsPetsUlta Beauty at TargetShop by CommunityTarget OpticalDealsClearanceNew ArrivalsGift Ideas for DadBack to SchoolCollegeTop DealsTarget Circle DealsWeekly AdShop Order PickupShop Same Day DeliveryRegistryRedCardTarget CircleFind Stores
An Introduction to Machine Learning - 3rd Edition by  Miroslav Kubat (Hardcover) - 1 of 1

An Introduction to Machine Learning - 3rd Edition by Miroslav Kubat (Hardcover)

$64.99Save $5.00 (7% off)

In Stock

Free & easy returns

Free & easy returns

Return this item by mail or in store within 90 days for a full refund.
Eligible for registries and wish lists

About this item

Highlights

  • This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms.
  • About the Author: Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years.
  • 458 Pages
  • Computers + Internet, Artificial Intelligence

Description



Book Synopsis



This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications.

The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.



From the Back Cover



This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications.

The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.



About the Author



Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks. Professor Kubat is also known for his many practical applications of machine learning, ranging from oil-spill detection in radar images to text categorization to tumor segmentation in MR images.

Dimensions (Overall): 9.21 Inches (H) x 6.14 Inches (W) x 1.06 Inches (D)
Weight: 1.86 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 458
Genre: Computers + Internet
Sub-Genre: Artificial Intelligence
Publisher: Springer
Theme: General
Format: Hardcover
Author: Miroslav Kubat
Language: English
Street Date: September 27, 2021
TCIN: 84910132
UPC: 9783030819347
Item Number (DPCI): 247-33-4675
Origin: Made in the USA or Imported
If the item details aren’t accurate or complete, we want to know about it.

Shipping details

Estimated ship dimensions: 1.06 inches length x 6.14 inches width x 9.21 inches height
Estimated ship weight: 1.86 pounds
We regret that this item cannot be shipped to PO Boxes.
This item cannot be shipped to the following locations: American Samoa (see also separate entry under AS), Guam (see also separate entry under GU), Northern Mariana Islands, Puerto Rico (see also separate entry under PR), United States Minor Outlying Islands, Virgin Islands, U.S., APO/FPO, Alaska, Hawaii

Return details

This item can be returned to any Target store or Target.com.
This item must be returned within 90 days of the date it was purchased in store, delivered to the guest, delivered by a Shipt shopper, or picked up by the guest.
See the return policy for complete information.

Q: What is the format of this machine learning textbook?

submitted by AI Shopping Assistant - 1 month ago
  • A: The textbook is available in hardcover format, making it durable for frequent use and reference.

    submitted byAI Shopping Assistant - 1 month ago
    Ai generated

Q: Who is the author of this machine learning textbook?

submitted by AI Shopping Assistant - 1 month ago
  • A: The author is Miroslav Kubat, an Associate Professor at the University of Miami with over 25 years of experience in machine learning.

    submitted byAI Shopping Assistant - 1 month ago
    Ai generated

Q: What topics are covered in this machine learning textbook?

submitted by AI Shopping Assistant - 1 month ago
  • A: The book covers Bayesian classifiers, decision trees, neural networks, reinforcement learning, and more advanced techniques like deep learning and hidden Markov models.

    submitted byAI Shopping Assistant - 1 month ago
    Ai generated

Q: What is the primary audience for this textbook?

submitted by AI Shopping Assistant - 1 month ago
  • A: The textbook is suggested for readers aged 22 years and up, making it suitable for university students and professionals.

    submitted byAI Shopping Assistant - 1 month ago
    Ai generated

Q: Does the book include practical applications of machine learning?

submitted by AI Shopping Assistant - 1 month ago
  • A: Yes, the book provides practical advice and discussions on applications, including examples and illustrations to enhance understanding.

    submitted byAI Shopping Assistant - 1 month ago
    Ai generated

Additional product information and recommendations

Discover more options

Frequently bought together

Best-selling Computers & Technology Books

Get top deals, latest trends, and more.

Privacy policy