A key aspect of intelligent systems is their ability to learn from data or past experience. Modern methods for data analysis also draw heavily on techniques for learning patterns in data. This tutorial introduces many common methods for *machine learning* that are used in the fields of intelligence science and data science. The video lectures explore some of the basic concepts and theory underlying the behaviour of various learning methods and their application to different kinds of problems. This theory is complemented by hands-on computer labs in the MATLABÂ® computing environment, to explore the behaviour of machine learning methods in practice.

Visualization of the results of *principal components analysis* applied to high-dimensional data capturing visual properties of handwritten digits. The data was reduced to three dimensions that capture most of the variation in the original data, roughly segregating the data into the corresponding digits, as portrayed by the different colors of the data points. (Image courtesy of Lorenzo Rosasco, used with permission.)

## Unit Activities

### Useful Background

- Introductions to calculus, linear algebra, probability and statistics
- Introduction to computer programming and MATLAB (see our MATLAB Tutorial)