Chapter 13. Machine Learning

What Is Machine Learning

The goal of machine learning (ML)[228] is to turn data into information. After learning from a collection of data, we want a machine to be able to answer questions about the data: What other data is most similar to this data? Is there a car in the image? What ad will the user respond to? There is often a cost component, so this question could become: “Of the products that we make the most money from, which one will the user most likely buy if we show them an ad for it?” Machine learning turns data into information by extracting rules or patterns from that data.

Training and Test Set

Machine learning works on data such as temperature values, stock prices, color intensities, and so on. The data is often preprocessed into features. We might, for example, take a database of 10,000 face images, run an edge detector on the faces, and then collect features such as edge direction, edge strength, and offset from face center for each face. We might obtain 500 such values per face or a feature vector of 500 entries. We could then use machine learning techniques to construct some kind of model from this collected data. If we only want to see how faces fall into different groups (wide, narrow, etc.), then a clustering algorithm would be the appropriate choice. If we want to learn to predict the age of a person from (say) the pattern of edges detected on his or her face, then a classifier algorithm would be appropriate. To meet our goals, machine ...

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