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.
