Lecturers: Gavin Brown and Ross King.
What is this module about?
This is module 1 in the Learning from Data theme.
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, and probability distributions. The emphasis is on constructing these models
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.
I studied ML in my undergrad degree, so should I do this module?
If you have sat an undergraduate ML course (particularly my COMP24111) then you may feel you
know all this material. In fact we will cover virtually the same topics - however, you almost certainly will not have
covered this material in the same depth as we will cover it. We will study why and how these methods
work, at a very deep level. This is not a course on how to use ML techniques. It is a course on the
deeper aspects, and will prepare you for more advanced modules later in
the year, as well as MSc projects next summer.
Do I have to know any maths?
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
Do I have to do anything before the first lecture?
YES. It will significantly benefit you to study the MATLAB tutorial.
Or you can try the manual which some people prefer.