Computational Statistics and Machine Learning MRes

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the areas of computational statistics and machine learning (CSML). Students will have the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research. They also undertake a nine-month research project which enables the department to more fully assess their research potential.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (30 credits), three optional modules (45 credits) and a dissertation (105 credits).

• Investigating Research
• Researcher Professional Development

Student select three modules from the following:

• Advanced Deep Learning and Reinforcement Learning
• Advanced Topics in Machine Learning
• Applied Bayesian Methods
• Approximate Inference and Learning in Probabilistic Models
• Graphical Models
• Information Retrieval and Data Mining
• Introduction to Deep Learning
• Introduction to Machine Learning
• Inverse Problems in Imaging
• Machine Vision
• Probabilistic and Unsupervised Learning
• Selected Topics in Statistics
• Statistical Computing
• Statistical Inference
• Statistical Models and Data Analysis
• Supervised Learning

All students undertake an independent research project which culminates in a substantial dissertation.

The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with assistance from demonstrators. Students liaise with their academic or industrial supervisor to choose a study area of mutual interest for the research project. Performance is assessed by unseen written examinations, coursework and the research dissertation.