quick look at machine learning in unsupervised learningwe are given set of feature vectors but no labels the goal of unsupervised learning is to uncover latent structure in the set of feature vectors for examplegiven the set of presidential feature vectorsan unsupervised learning algorithm might separate the presidents into tall and shortor perhaps into american and french the most popular unsupervised learning techniques are designed to find clusters of similar feature vectors geneticistsfor exampleuse clustering to find groups of related genes many popular clustering methods are surprisingly simple we will present the most widely used algorithm later in this firsthoweverwe want to say few words about feature extraction feature vectors the concept of signal-to-noise ratio (snris used in many branches of engineering and science the precise definition varies across applicationsbut the basic idea is simple think of it as the ratio of useful input to irrelevant input in restaurantthe signal might be the voice of your dinner dateand the noise the voices of the other diners if we were trying to predict which students would do well in programming courseprevious programming experience and mathematical aptitude would be part of the signalbut gender merely noise separating the signal from the noise is not always easy and when it is done poorlythe noise can be distraction that obscures the truth in the signal the purpose of feature extraction is to separate those features in the available data that contribute to the signal from those that are merely noise failure to do an adequate job of this introduces two kinds of problems irrelevant features can lead to bad model the danger of this is particularly high when the dimensionality of the data ( the number of different featuresis large relative to the number of samples irrelevant features can greatly slow the learning process machine learning algorithms are often computationally intensiveand complexity grows with both the number of examples and the number of features the goal of feature extraction is to reduce the vast amount of information that might be available in examples to information from which it will be productive to generalize imaginefor examplethat your goal is to learn model that will predict whether person likes to drink wine some attributese age and the nation in which they liveare likely to be relevant other attributese whether they are left-handedare less likely to be relevant feature extraction is difficult in the context of supervised learningone can try to select those features that are correlated with the labels of the examples in unless your dinner date is exceedingly boring in which caseyour dinner date' conversation becomes the noiseand the conversation at the next table the signal