Machine Learning

Course options and guidance for the Machine Learning topic area

What will I learn from courses in this topic?

Increasing amounts of data are being captured, stored and made available electronically. Courses in this topic and the related topics listed below will train students in techniques to analyse, interpret and exploit such data, and to understand when particular methods are suitable and/or applicable. Machine learning techniques include probabilistic and statistical modelling, pattern recognition and neural networks, and exploratory analysis or data mining.

I don't want to specialise, but I feel I need to learn something about machine learning. What should I do?

Machine learning is a very popular area right now and can be useful in other areas of Informatics, but you shouldn't feel you must do it just because others are. If you do want to learn a bit about it, we recommend one of two options:

1. Take Applied Machine Learning, which provides a general background and introduction to the common tools and methods. Please check that you satisfy the mathematical and programming requirements first - see 'Maths' section below.

2. Take one or more of the courses listed under "Applications and related areas" below. Although they don't have "machine learning" in the title, most of these courses will introduce you to some basic machine learning methods and how they apply to a particular domain.

How can I best prepare for courses in this topic?

Maths

Machine learning uses several university-level mathematical tools, in particular linear algebra, multivariate calculus, and probability theory. Without a reasonable level of fluency in these tools, it is not possible to understand advanced material or do research in the field. It is in your interest to be honest with yourself: if the level of background is too great to prepare, then take alternative courses.

If you click on any of the course links below you may find a list of maths requirements in the "other requirements" box of the course descriptor.

Programming

You will need to be able to write programs that handle matrices and vectors. Most of the machine learning and AI courses in the School use Python, and array-based libraries such as NumPy or PyTorch. If you haven't programmed with a matrix-based language or library before, we recommend you practice with one as much as you can before you arrive. The background section of the notes for Machine Learning and Pattern Recognition (MLPR) gives possible starting points. As long as you are already fluent in some programming language (such as Java or C++), you should be able to pick up what's required.

What courses are available and how do I choose?

If you want to specialize in machine learning, we recommend taking at least 30 credits from the "Foundations and methods" list below. In addition, we recommend taking at least 20 credits from one of the lists of Related Topics (either "Applications" or "Algorithms and Infrastructure"). These courses will broaden your knowledge of how machine learning relates to other areas, and may also help you by providing more flexibility when it comes to choosing a dissertation topic.

Foundations and methods

Please click on the course links to check the "other requirements" in the descriptor of each course to be sure it is suitable for you. You can select your courses before you arrive, and will be able to discuss your choices with your Student Adviser/Cohort Lead.

Semester 1:

Applied Machine Learning INFR11211 (20 credits):

Semester 2:

Methods for Casual Inference INFR11207 (10 credits)

This course explores causal inference techniques that allow us to move away from merely associational statements and instead towards cause-effect statements. This is a relatively advanced course and students are expected to be familiar with foundations of probability, statistics, and calculus. PMR is a recommended co-requisite.

Probabilistic Modelling and Reasoning INFR11134 (20 credits):

PMR covers the foundations of probabilistic modelling in a machine learning context.

Full year:

Machine Learning Practical INFR11132 (20 credits):

This course focusses on the implementation and evaluation of deep learning methods. Most students specialising in machine learning should take this course. But please note that it is not a stand-alone introduction to machine learning, and also assumes familiarity with the maths commonly used in machine learning as well as prior programming experience. If you don't have prior machine learning experience or have little programming background it will move very quickly and be very time-consuming. You might do better to plan on taking an alternative applications course that includes considerable material on neural networks starting in semester 2 (e.g., NLU+ or ASR in the Natural Language Processing topic area).