Supervised learning (machine learning) takes a known set of input data and known(labeled) responses to the data, and seeks to build a predictor model that generates reasonable predictions for the response to new data.
For example, given data on how 1000 medical patients respond to an experimental drug (such as effectiveness of the treatment, side effects, etc.), discover whether there are different categories or “types” of patients in terms of how they respond to the drug, and if so what these categories are. This is a supervised learning task.
Supervised learning is usually used to solve the following three types of problems:
- Classification problem: When the data are being used to predict a category, supervised learning is also called classification. This is the case when assigning an image as a picture of either a ‘cat’ or a ‘dog’. When there are only two choices, this is called two-class or binomial classification. When there are more categories, as when predicting the winner of the NCAA March Madness tournament, this problem is known as multi-class classification.
- Regression problem: when a value is being predicted, as with stock prices.
- Anomaly detection problem: Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it’s not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different.
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. The clusters are modeled using a measure of similarity which is defined upon metrics such as Euclidean or probabilistic distance.
For example, given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different clusters of such patients for which we might tailor separate treatments. This is an unsupervised learning task.
Reinforcement Learning is the 3rd area in Machine Learning. It’s different from supervised learning and unsupervised learning. Read ML101: Reinforcement Learning.