Unsupervised Learning

An overarching goal in Machine Learning is to model the distribution of data generated by a process. Considering from a system point of view, if there is no candidate data item that could be designated as the output of the system, learning about this distribution is called unsupervised learning. Finding clusters of data where samples are concentrated, detecting outliers coming from a multivariate distribution, approximating the multivariate density of a set of random variables are unsupervised learning problems. A foundation in algorithms and techniques of unsupervised learning is a must for aspiring or practicing Data Scientists.