The Master Algorithm is a reasonable overview of the growing field of machine learning. The author explores the idea of there being a master algorithm which could explain everything given enough data. The idea seems a bit over the top when initially phrased but the real purpose of the book is to introduce ideas used in machine learning and how they work and are used to solve problems.
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.
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A. Menon
Good overview of machine learning techniques for non-expert but overall a mixed book
10 November 2015 -
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Awesome and Unique Comprehensive Yet Readable Overview of ML
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Dennis Muzza
Great start but got lost halfway through
1 January 2019 -
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