Prospective students
Key facts
- Admission status: Closed for entry in 2024-25.
- Expected length: 2 years.
- Study mode: Part-time
- Location: Online
- Expected start: 30 September 2024.
Contact us
Contact us with further enquiries: ml-online-msc@imperial.ac.uk
Coursera
Find more information on Coursera
Applications
Applications for Autumn 2024 entry are now closed.
The course
In this programme you will develop an in-depth understanding of the mathematical and statistical foundations underlying modern machine learning methods, alongside invaluable practical skills and guided experience in applying them to real-world problems. 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 modelling, deep learning, unstructured data processing and anomaly detection. You will not only build a strong foundation in Mathematics and Statistics, giving you confidence in your analytical skills, but you will also acquire expertise in implementing scalable machine learning solutions using industry-standard tools such as PySpark, ensuring that no data is too big or too complex for you.
You will also have the opportunity to broaden your horizons through one of the first of its kind study of ethical issues 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.
Course Overview
This online, part-time master’s degree programme integrates mathematical rigor with practical machine learning and data science skills, preparing students for advanced roles in industry or research.
The programme covers a range of topics from both theoretical and applied perspectives, with a focus on statistical estimation, prediction and anomaly detection. Topics range from the fundamentals of probability and decision theory through to advanced deep learning and reinforcement learning techniques, covering supervised and unsupervised learning, Bayesian methods and unstructured data processing.
This programme will enhance your analytical abilities in mathematics and statistics. You will gain expertise in tackling complex data by implementing scalable solutions using industry-standard tools, including PySpark. You will also explore the limitations of machine learning methods and learn how to ethically apply these techniques to your work.
Finally, you will have the opportunity to apply the knowledge you have gained from the taught programme through an extensive research project, carried out in collaboration with a member of academic staff.
All learning is delivered online.