How can we represent data on a computer and use it to learn to perform useful tasks? This lecture reviews some simple classification and regression rules, discusses under- and over-fitting and emphasises the utility of defining objective functions for learning. There is also a short overview of Bayesian learning, and some practical tips for pre-processing and visualizing data. The lecture ends with a brief mention of unsupervised learning and related topics.