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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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"Pedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be."―Walter Isaacson, author of Steve Jobs and The Innovators
"An impressive and wide-ranging work that covers everything from the history of machine learning to the latest technical advances in the field."―Daily Beast
"Domingos writes with verve and passion."―New Scientist
"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
"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 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
"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."―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 must have to learn machine learning without equation. It will help you get the big picture of the several learning paradigms. Finally, the provocative idea is not only intriguing, but also very well argued."―Data Mining Research
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.
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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 book is split into multiple chapters which start from discussing abstractly the master algorithm and then move on to some of the philosophical issues associated with using such algorithms. In particular the author discusses at the core of believing in pattern recognition algorithms is belief in inductive reasoning. The author discusses human learning and gets into some neuroscience and how neural networks are constructed. The reader gets a vague sense of Hebbian learning and how neuron weighting are at the core of neural networks. The author spends a lot of time discussing various approaches in machine learning and gives the reader an intuitive feel of Bayesian learning. The author was an originator in a particular algorithm called naïve Bayes which greatly simplified solutions to certain problems and so the author introduces his ideas to the reader. Other machine learning ideas are introduced like genetic programming and multivariable regression. The author also discusses other machine learning algorithms which turn data into a vector and then look for close neighbors of the vector to classify the input. The author also spends some time on how unsupervised learning would look. The book combines computer science ideas and intuition and tries to use a fictitious robot as the means to convey ideas about how a computer would learn. The author finally introduces his own master algorithm called alchemy which combines most of the models described in the book. The reader really gets little actual sense of what's going on in the algorithm as the author qualifies one needs a PhD in computer science.
The Master Algorithm is the first book I have seen which introduces some of the ideas being used in machine learning to a general audience. It does so quite well and most of the ideas are absorbable. At the same time there are a few too many instances where the author is self promoting talking about all of the brilliant ideas he has had which have reshaped the field and how other areas of AI research of the past or Kurzweil and his singularity concept are idiotic. Despite probably being right in much of his analysis its arguing with no one on the other side and unproductive. Also the flavor of the writing is odd - it turns into some fantasy literature at times as though that makes the subject more digestible and in fact makes it more irritating. I enjoyed reading aspects of the book and do think the parts on what different schools of machine learning focus on are well written for a non expert, unfortunately there are many other parts of the book which one wants to get through as quickly as possible.