Machine Learning - Computer Science at UBC

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.

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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  Machine, Learning, Supervised, Machine learning, Machine learning machine learning, Supervised learning

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