Machine Learning Revised And(Revised, Updated Edition)

Authors:

Ethem Alpaydin

Type:Hardcover/ PaperBack / Loose Leaf
Condition: Used/New

In Stock: 2 Left

Shipment time

Expected shipping within 2 - 3 Days
Access to 35 Million+ Textbooks solutions Free
Ask Unlimited Questions from expert AI-Powered Answers 30 Min Free Tutoring Session
7 days-trial

Total Price:

$0

List Price: $14.09 Savings: $14.09 (100%)
Access to 30 Million+ solutions
Ask 50 Questions from expert AI-Powered Answers 24/7 Tutor Help Detailed solutions for Machine Learning Revised And

Price:

$9.99

/month

Book details

ISBN: 0262542528, 978-0262542524

Book publisher: The MIT Press

Offer Just for You!: Buy 2 books before the end of January and enter our lucky draw.

Book Price $0 : A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of "the new AI." This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin, author of a popular textbook on machine learning, explains that as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.