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Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs. More and more, these algorithms work by learning from the trails of data we leave in our newly digital world. Like curious children, they observe us, imitate, and experiment. And in the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.
Machine learning is the automation of discovery—the scientific method on steroids—that enables intelligent robots and computers to program themselves. No field of science today is more important yet more shrouded in mystery. Pedro Domingos, one of the field’s leading lights, lifts the veil for the first time to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He charts a course through machine learning’s five major schools of thought, showing how they turn ideas from neuroscience, evolution, psychology, physics, and statistics into algorithms ready to serve you. Step by step, he assembles a blueprint for the future universal learner—the Master Algorithm—and discusses what it means for you, and for the future of business, science, and society.
If data-ism is today’s rising philosophy, this book will be its bible. The quest for universal learning is one of the most significant, fascinating, and revolutionary intellectual developments of all time. A groundbreaking book, The Master Algorithm is the essential guide for anyone and everyone wanting to understand not just how the revolution will happen, but how to be at its forefront.
- Sales Rank: #12000 in Books
- Published on: 2015-09-22
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.13" w x 6.13" l, 1.26 pounds
- Binding: Hardcover
- 352 pages
Review
“Domingos writes with verve and passion, and the book has a strong narrative.”
—New Scientist
“[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.”
—The Economist
“Wonderfully erudite, humorous, and easy to read.”
—KDNuggets
“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.”
—Kirkus Reviews
“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
“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 middle—from executives to college students—this 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
“Machine learning is the single most transformative technology that will shape our lives over the next fifteen years. This book is a must-read—a bold and beautifully written new framework for looking into the future.”
—Geoffrey Moore, author of Crossing the Chasm
“This is an incredibly important and useful book. Machine learning is already critical to your life and work, and will only become more so. Finally, Pedro Domingos has written about it in a clear and understandable fashion.”
—Thomas H. Davenport, Distinguished Professor, Babson College and author of Competing on Analytics and Big Data @ Work
“Machine learning is a fascinating world never before glimpsed by outsiders. Pedro Domingos initiates you to the mysterious languages spoken by its five tribes, and invites you to join in his plan to unite them, creating the most powerful technology our civilization has ever seen.”
—Sebastian Seung, Professor, Princeton, and author of Connectome
“A delightful book by one of the leading experts in the field. If you wonder how AI will change your life, read this book.”
—Sebastian Thrun, Research Professor, Stanford, Google Fellow and Inventor of the Self-Driving Car
About the Author
Pedro Domingos is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. A fellow of the Association for the Advancement of Artificial Intelligence, he lives near Seattle.
Most helpful customer reviews
102 of 107 people found the following review helpful.
This book isn't perfect, but I'm still giving it 5 stars because ...
By Karl Rosaen
This book isn't perfect, but I'm still giving it 5 stars because it provides a better overview of the entire field of Machine Learning than any other book I've come across. My background is in computer science and software engineering and I've been interested ML as more of a hobbyist and outside observer for a few years (reading some books, taking Andrew NG's coursera course), just recently dabbling in some applications professionally. What I was still missing before reading this book was as high a level understanding of where all of the models and technique in the field of ML fit. Other books describe the difference between supervised and unsupervised learning, but this book goes further in describing how, say, decisions trees, support vector machines and deep neural networks fit compared to each other and within which subfields statistics play a larger role than others.
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.
58 of 63 people found the following review helpful.
thought provoking but biased
By Tao Xu
It is a thought provoking book, covering 5 tribes of supervised learning, a bit of unsupervised learning, reinforcement learning and other stuff like chunking and relational learning, etc. It has lots of speculations about the master algorithm which could learn everything. The climax of the book is the circled map of master algorithm. For each topic, it talks about the most important ideas, algorithms and applications, and how are they connected with other tribes. It also emphasize a few common principals: overfit, curse of dimensionality, explore and exploitation, structure learning and parameters learning, etc. The author is very good at explaining in a few simple sentences the gist of an advance algorithm e.g. TD(lambda) and value function approximation in reinforcement learning, message passing, MCMC and Kalman filtering in bayesian, etc.
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.
41 of 44 people found the following review helpful.
A Bird's Eye View of Machine Learning's History
By Eclectic Reader
The Master Algorithm is a book about Machine Learning. But while it claims to be about its future, the focus is more on its past. Domingos' gives us a tour of the five different Machine Learning fiefdoms (Evolutionaries, Connectionists, Symbolists, Bayesians, and Analogizers ), how they connect, and what their histories are.
If you're not familiar with machine learning at all, it's a broad but interesting overview. At times the discussion gets a little too technical. Sentences like "...we can view what SVMs do with kernels, support vectors, and weights as mapping the data to a higher-dimensional space and finding a maximum-margin hyperplane in that space" (pg 196) make it clear Mr. Domingos is no Malcolm Gladwell.
Another issue I had with the book is Mr. Domingos' tendency to overstate the current state of machine learning. Early on he claims Obama's use of machine learning was instrumental in his 2012 win against Mitt Romney. "The candidate with the best voter models wins, like Obama against Romney" (Prologue, pg xiv). I don't know of a single political scientist that would agree with this. Obama won the popular vote in 2012 by nearly four percentage points. That's far too high a margin to be attributable to any ground game.
Mr. Domingos also has a tendency to insert his own work into the book. I could have done without the self promotion.
If you're really interested in AI, but have no background in it, the middle of the book provides a broad overview of it's many sub-disciplines. I'm just not sure I can say it's worth it.
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