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
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Customer Reviews

Most Helpful
5 out of 5 stars
By Mr Phillip Ash on 12-07-17

awesome background and nice summary of current tec

A great summary of the different types of machine learning algorithm. Great for someone looking to get a good overview understanding :)

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3 of 4 people found this review helpful

3 out of 5 stars
By Anonymous User on 09-02-17

Good technical overview, but shallow on ethics

This was a useful book to understand the basic technical elements of machine learning, but the author gave very short thrift to the ethics and politics of machine learning. The author claims the book is a neutral overview, but very clearly has a vested interest in wanting to believe machine learning is - on net - always positive. The hidden bias, revealed in many of the examples, was towards corporate applications of machine learning to sell more products by extracting data from people. This should at least have been noted

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8 of 18 people found this review helpful

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Customer Reviews

Most Helpful
1 out of 5 stars
By pandrenyc on 01-12-16

Narrator not suited to the material

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.

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12 of 13 people found this review helpful

1 out of 5 stars
By Amazon Customer on 23-01-17


A little info about machine learning at the end, after wadding through chapters of shallow pedantism about the history of computing.

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10 of 11 people found this review helpful

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