In this programme, you will develop an in-depth understanding of machine learning models, alongside invaluable practical skills and guided experience in applying them to real-world problems. You will also have the opportunity to broaden your horizons through one of the first of its kind study of ethical topics posed by machine learning. You will graduate with an ability to go beyond the algorithms and turn data into actionable insights, contribute to strategic decision making in your organisation and become a responsible member of this rapidly growing profession.
What is Machine Learning?
Machine learning is the scientific study of algorithms that computer systems use to perform specific tasks without using explicit instructions, relying on patterns and inference instead. It is seen as being closely related to Artificial Intelligence and has a number of applications in products and services used in daily life.
The curriculum is designed to propel your engineering or data science career forward, allowing you to choose the path that’s right for you, be that a role as a data scientist, a machine learning engineer, or a computational statistician.
With hands-on projects, you’ll build a portfolio to showcase your new skills in everything from probabilistic modeling, deep learning, unstructured data processing and anomaly detection. You will build a strong foundation in mathematics and statistics, giving you confidence in your analytical skills, but also acquire expertise in implementing scalable machine learning solutions using industry-standard tools such as PySpark and PyTorch, ensuring that no data is too big or too complex for you.
Who is this degree for?
This degree offers multiple pathways to meet the needs of students with multiple backgrounds — both students just starting a career in data science, and those already working in roles such as senior data analysts, bioinformatics scientists, statisticians or business analysts.
Graduates are likely to pursue roles as data scientists, machine learning engineers, natural language processing engineers, data engineers, bioinformatics or health data scientists, AI engineers, or software engineers. Possibilities extend beyond this list, however, as machine learning is slowly becoming indispensable in other fields, such as journalism or even tourism.
This is a rigorous programme: applicants are expected to have a quantitative undergraduate degree in a subject like computer science, math, statistics, economics, or physics.
Admission is performance-based, so there are no prerequisites or an application. Students simply need to pass a series of courses for admission to the degree programme.