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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World Hardcover – September 22, 2015
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Machine learning, known in commercial use as predictive analytics, is changing the world. This riveting, far-reaching, and inspiring book introduces the deep scientific concepts to even non-technical readers, and yet also satisfies experts with a fresh, profound perspective that reveals the most promising research directions. It's a rare gem indeed.”
Eric Siegel, founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
With terms like Machine Learning' and Big Data' regularly making headlines, there is no shortage of hype-filled business books on the subject. There are also textbooks that are too technical to be accessible. For those in the middlefrom executives to college studentsthis is the ideal book, showing how and why things really work without the heavy math. Unlike other books that proclaim a bright future, this one actually gives you what you need to understand the changes that are coming.”
Peter Norvig, Director of Research, Google and coauthor of Artificial Intelligence: A Modern Approach
[The Master Algorithm] opens the doorway to a world many of us never see or think about, though it has a tremendous impact on our daily lives.”
Shelf Awareness for Readers
[The Master Algorithm] does a good job of examining the field's five main techniques...The subject is meaty and the author has a knack for introducing concepts at the right moment.”
Wonderfully erudite, humorous, and easy to read.”
Domingos is a genial and amusing guide, who sneaks us around the backstage areas of the science in order to witness the sometimes personal (and occasionally acrimonious) tenor of research on the subject in recent decades... This is a highly inclusive book, aimed at a wide range of readers from the merely curious to those who might be interested in pursuing a career in the field. Descriptions and discussions are presented with a commendable lack of jargon and the examples are clear and accessible.”
Times Higher Education
An exhilarating venture into groundbreaking computer science.”
Booklist, starred review
[An] enthusiastic but not dumbed-down introduction to machine learning... lucid and consistently informative.... With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself. Readers...will discover fascinating insights.”
This book is a sheer pleasure, mixed with education. I am recommending it to all my students, those who studied machine learning, those who are about to do it and those who are about to teach it. The author succeeds not only in presenting an accurate and entertaining journey through the methodological ideas behind machine learning but also in embedding those ideas in a colorful tapestry of philosophical questions concerning the ultimate capacity of man to emulate itself. A must read for both realists and futurists.”
Judea Pearl, Professor of Computer Science, UCLA and winner of the A. M. Turing Award
Starting with the audacious claim that all knowledge can be derived from data by a single master algorithm,' Domingos takes the reader on a fast-paced journey through the brave new world of machine learning. Writing breezily but with deep authority, Domingos is the perfect tour guide from whom you will learn everything you need to know about this exciting field, and a surprising amount about science and philosophy as well.”
Duncan Watts, Principal Researcher, Microsoft Research, and author of Six Degrees and Everything Is Obvious *Once You Know the Answer
The holy grail of computer science is a machine that can teach itself, as we humans do, from experience. Machine learning could help us do everything from curing cancer to building humanoid robots. Pedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be.”
Walter Isaacson, author of Steve Jobs and The Innovators
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Top Customer Reviews
The book also puts many techniques in historical perspective that I found very helpful, such as the rise, fall and rise again of deep neural networks with support vector machines taking a lead as the hottest technique in between (while also making clear that SVMs are a useful technique with unique strengths today). Finally, it makes clear that these techniques are not all competing for being the best overall at everything, but that they can be used quite complementary and/or they have unique strengths within certain problem domains. The book accomplishes all of this through a survey of broad subfields of ML, how each has attempted to be *the* master algorithm, has fallen short in some ways, but remains the best at some things and could play a role in the state of the art master algorithm (while acknowledging we're not quite there yet). So while the term 'master algorithm' is somewhat of a gimmick (as he acknowledges), it's a good way to think about what ML is attempting to accomplish as a field: building working, adaptive software systems with less and less human assistance by learning from data, and to see how many specific techniques have played a role in progress.
What I don't know is how accessible this book might be to someone who's less technical. I think the first couple chapters would be a great read for anyone with a general interest, making clear how ML differs from the traditional software / automation that has brought us so far, but it could be that the details within the rest of the chapters that go into more depth would be too in the weeds.
I've also read some other reviews from technical readers that assert the book lacks enough depth to be helpful, but this wasn't the case for me, in fact the level of detail was perfect—just deep enough to match with details I'd skimmed before in previous surveys of the field yet not so deep that I couldn't get through and enjoy the chapters in a casual evening read. The author also explained some concepts better than I've read anywhere else before, such as the debate is between frequentist and bayesian statisticians.
I have read a few textbooks on machine learning (intro to Statistical Learning by Hastie etc) and so I would say that my knowledge of ML is at the "textbook overview" level. Since I am not a ML practitioner, I may not be the best judge of a book such as this one, it was a fairly difficult read, and I know I need to read the book a second time to get an even greater appreciation and understanding of the concepts covered by the author. That being said, it was a very enjoyable book. The book was very different from any ML book that I've read or checked out either at a bookstore or online. I think one needs to have some knowledge of ML to appreciate the book - concepts like supervised learning, unsupervised learning, Bayesian inference, support vector machines, neural networks, etc .... The book deserves a 5-star rating because it added a lot of value to my understanding of ML, and increased my desire and curiosity to learn more about the field of ML ....
The author is a bit biased, perhaps 80+% Bayesian, claiming Markov Logic Network is the secret ingredient of master algorithm. I found that part the most unsatisfactory in book, not convinced by the arguments.
Most Recent Customer Reviews
A little bit hard to follow the writer's thoughts from time to time, the way the...Read more