Under unsupervised learning, clustering or cluster analysis is first studied. Clustering methods are grouped into non-hierarchical (including K-means clustering) and hierarchical clustering. Self-organizing maps can be used as a clustering method or as a discrete non-linear principal component analysis method. Autoencoders are neural network models that can be used for non-linear principal component analysis. Non-linear canonical correlation analysis can also be performed using neural network models.

This chapter introduces machine learning in contemporary artificial intelligence. The first section looks at an expert system developed in the early days of AI research – ID3, which employs a decision-tree-based algorithm. The second section looks at advances in deep learning, which has transformed modern machine learning. We introduce a deep learning model inspired by the mammalian visual system, illustrating how it can extract hierarchical information from the raw data. The third section addresses two examples of neural networks -- autoencoders and convolutional neural networks, which can feature in layers of deep learning networks. The last section looks at a distinct type of machine learning -- reinforcement learning. We explain how deep reinforcement learning has made possible the two most spectacular milestones in artificial intelligence - AlphaGo and AlphaGo Zero.