Course Lecturers: Gavin Brown (weeks 1-2) and Magnus Rattray (weeks 3-5)
What is Machine Learning?
Machine learning is concerned with creating mathematical "data structures" that allow a computer to exhibit behaviour that would normally require a human. Typical applications might be spam filtering, speech recognition, medical diagnosis, or weather prediction. The data structures we use (known as "models") come in various forms, e.g. trees, graphs, algebraic equations, probability distributions. The emphasis is on constructing these models automatically from data---for example making a weather predictor from a datafile of historical weather patterns. This course will introduce you to the concepts behind various Machine Learning techniques, including how they work, and use existing software packages to illustrate how they are used on data.Is this course right for you?
This course has a fairly high mathematical content. We will be making extensive use of matrix algebra, probability theory, and calculus.Take a look at the Mathematics Primer for the course. It should be stressed that you are not expected to have all this before the start - however if you think with some hard work you could get to grips with most of it, then fine, if not, then maybe the course is not for you.