Unsupervised learning

Unsupervised learning works with unlabeled data, so we don't need the actual output, only the input. It tries to find patterns in the data and reacts based on those commonalities, dividing the input into clusters:

Usually, unsupervised learning is often used in conjunction with supervised learning to reduce the input space and focus the signal in the data on a smaller number of variables, but it has other goals as well. From this point of view, it is more applicable than supervised learning as sometimes tagging the data is expensive or not reliable.

Common unsupervised learning techniques are clustering and principal component analysis (PCA), independent component analysis (ICA), and some neural networks such as Generative Adversarial Networks (GANs) and Autoencoders (AEs). We will explore the last two in more depth later on in this book.