Name: Machine Learning (The mit Press Essential Knowledge Series) (en Inglés)
Price: 24.85 USD
Availability: InStock
Author: Alpaydin, Ethem
ISBN: 9780262542524

portada Machine Learning (The mit Press Essential Knowledge Series) (en Inglés)

Machine Learning (The mit Press Essential Knowledge Series) (en Inglés)

Formato

Libro Físico

Autor

Ethem Alpaydin

Editorial

MIT Press

Año

2021

Idioma

Inglés

N° páginas

280

Encuadernación

Tapa Blanda

Dimensiones

17.5 x 12.7 x 1.8 cm

Peso

0.25 kg.

ISBN13

9780262542524

Categorías

Historia de la tecnología y la ingeniería
Aprendizaje automático («machine learning»)

Machine Learning (The mit Press Essential Knowledge Series) (en Inglés)

Ethem Alpaydin (Autor) · MIT Press · Tapa Blanda

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Reseña del libro "Machine Learning (The mit Press Essential Knowledge Series) (en Inglés)"

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.