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Machine Learning A Probabilistic Perspective Kevin P. Murphy The MIT Press Cambridge, Massachusetts London, England 1 Introduction Machine Learning : what and why? We are drowning in information and starving for knowledge. John Naisbitt. We are entering the era of big data. For example, there are about 1 trillion web pages1 ; one hour of video is uploaded to YouTube every second, amounting to 10 years of content every day2 ; the genomes of 1000s of people, each of which has a length of 109 base pairs, have been sequenced by various labs; Walmart handles more than 1M transactions per hour and has databases containing more than petabytes ( 1015 ) of information (Cukier 2010); and so on. This deluge of data calls for automated methods of data analysis, which is what Machine Learning provides.

1.1.1 Types of machine learning Machine learning is usually divided into two main types. In the predictive or supervised learning approach, the goal is to learn a mapping from inputs x to outputs y, given a labeled set of input-output pairs D = {(x i,y i)}N i=1. Here D is called the training set, and N is the number of training examples.

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