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.
