MSc Advanced Machine Learning at University of Bath

Programme overview

Go beyond simply using AI tools. Develop the expertise to design and deploy intelligent systems that are transforming industries from healthcare and finance to autonomous vehicles and entertainment. At Bath, you'll explore the foundations of machine learning before developing specialist expertise in reinforcement learning, computer vision and natural language processing.

What you will study

This course lasts 1 year. It starts in September 2027 and ends in 2028. Year 1 compulsory units include Applied machine learning, Foundational machine learning, Operational software technologies, Reinforcement learning 1, Understanding deep learning.

Career pathways

After graduating, you'll be well-placed for a variety of careers in industry. Throughout your studies, you will have access to a development programme via timetabled sessions and that includes employer events which will raise your awareness of the commercial opportunities available to a technologist.

Official programme page

Application information

Entry requirements

You should have a Bachelor’s honours degree or international equivalent, typically a high 2:2 or above. To apply for this course you should have an undergraduate degree in a numerate subject such as computer science, mathematics, physics, or engineering. You should also be able to demonstrate proficiency in mathematical topics such as calculus and linear algebra, possess a good knowledge of probability and statistics, and have a solid foundation in programming, particularly in Python. English language: IELTS: 6.5 overall with no less than 6.0 in all components.

Domestic applicants: GBP 15,200 a year (2027/28). Annual tuition fee for full-time 2025/26 entry onwards (MSc Advanced Machine Learning). Source

International applicants: GBP 35,250 a year (2027/28). Annual tuition fee for full-time 2025/26 entry onwards (MSc Advanced Machine Learning). Source

Intake: September 2027

Duration: 1 year

Apply through the official page: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=TDUCM-AM01&code2=0003

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