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 every day, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning - the foundation of efforts to process that data into knowledge - has also advanced.
In this audiobook, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general listener, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
©2016 Massachusetts Institute of Technology (P)2016 Gildan Media LLC
This book is a basic history of computing. It is so basic I'm shocked it's from MIT. Todays children instinctually understand the topics covered in this book. The title is misleading. It should simply read "The history of basic computing"
Really wanted to hear this book, but had to give up half way through chapter one. The theatrical delivery was too distracting and is not well sited to such a technical topic. Annunciation also very jarring for headphone listening. Disappointing.
Unfortunately, as the field development is vert rapid, the book will start to become outdated in 2017-2018. Some topics already feels missing some small, but important parts from 2016. Anyway, it is definitely recommended for listening, at least in 2017, ~2018.
Husband, father, IT Leader and Innovator specializing in Retail Energy pricing and costing systems
The author covers the topic well enough and the listener that is able to maintain focus will end up conversant in the topic of machine learning and its associated industries. I am fairly technical, and I had trouble staying engaged with this book. Information rich, but not evocative. Which is probably an unfair accusation to level at any text on such a geeky topic, but there you go dear listener.
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