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Machine Learning: The New AI (The MIT Press Essential Knowledge series), by Ethem Alpaydin
Ebook Machine Learning: The New AI (The MIT Press Essential Knowledge series), by Ethem Alpaydin
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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 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 book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, 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.
- Sales Rank: #17795 in Books
- Published on: 2016-10-07
- Original language: English
- Dimensions: 7.00" h x .44" w x 5.00" l, .0 pounds
- Binding: Paperback
- 224 pages
About the Author
Ethem Alpaydin is Professor in the Department of Computer Engineering at Bogaziçi University, Istanbul. He is the author of the widely used textbook Introduction to Machine Learning, now in its third edition (MIT Press).
Most helpful customer reviews
61 of 61 people found the following review helpful.
Very basic but good price point for a non technical overview and very up to date with big data
By Let's Compare Options Preptorial
This book is an introductory overview of Ethem's detailed text on ML. The text itself has gotten mostly mixed or bad reviews due to a lot of math and algorithms notated without a lot of detailed explanations, however, this is a general reader intro and doesn't go into math, algos in detail, trees, Bayesian logic or even pseudocode, it is more an up to date overview of the field as it exists at this writing. Alpaydin's expensive text, btw, is also available in a very inexpensive Asian edition here on Amazon if you want to brave that difficult book without a lot of investment (Introduction To Machine Learning 3Rd Edition).
The present volume is sortof a "ML for Dummies" only updated for the current craze with big data management. There is a lot of history and background that an experienced ML person will find too basic, but as a High School intro or general interested reader intro it is excellent. The chapters (only 7) begin with a history of computing in general, go into model fitting and stats explaining how big data has changed ML, pattern recognition specifically related to speech and facial recognition, neural nets and the brain as a model (without going into specific XOR or other examples that show, for example, how simple Python code can execute the basic steps of NN. There are Kindle volumes that do that very well: two of the best are: Introduction to the Math of Neural Networks and Code Your Own Neural Network: A step-by-step explanation. Along those lines, one of the best albeit expensive "try it yourself" volumes in NN is the wonderful: Make Your Own Neural Network, truly a hands on volume that even beginners can master.
The fifth chapter goes into clusters and argues that the old "AI" of expert systems which disappointed so many has now been salvaged by ML, Data Science and Big Data. The book concludes with some speculations on the future.
There are literally hundreds of books and texts on ML, but many of them are too complex and expensive (like Ethem's actual text albeit the Asian economy edition helps), or out of date. This great little intro fills a gap between the too detailed and the out of date if you want a good intro without any math or programming to the general field, put in context, with more up to date overviews of the interaction between learning algos and big data, which is the direction AI has taken now. Highly recommended for the right audience, not for techies, swings the pendulum to the far other side of Alpaydin's actual text (although partially based on it) which has been criticized as too technical. This is far from that.
TIP: Since this doesn't have math or code, ie no Latex or symbolic notation, it is ideal for a Kindle read, which is not true for a lot of the more technical texts-- just to save you a bit of investment.
16 of 17 people found the following review helpful.
No technical usefulness and odd style
By Mark on Amazon
The writing wanders around, the analogies don't quite fit, and it has no technical usefulness. I found it annoying to wade through what seemed like jumbles of ideas lumped together in paragraphs. Simple ideas are drawn out over long stretches of text and the style of writing is just strange in places.
5 of 5 people found the following review helpful.
A good place to start
By William H. Panning
Deliberately written for the non-specialist, Alpaydin's book provide a deliberately high-level summary of the main areas and specialties in machine learning. It is non-technical, and therefore ideal for the corporate executive or undergraduate student. For the non-specialist, it is a good starting point for a long road, and has adequate references to follow-up resources. Written by one of the luminaries in the field.
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