Computational Statistics and Machine Learning MSc teaches advanced analytical and computational skills for success in a data rich world
To find out core information about this degree, such as entry requirements, programme length and cost, visit the UCL prospectus site.
Students undertake modules to the value of 180 credits.
The programme consists of two core modules (30 credits), three to five optional modules (45 to 75 credits), one to three elective modules (15 to 45 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.
- Supervised Learning (COMP0078) (15 credits)
- Statistical Models and Data Analysis (STAT0028) (15 credits)
- MSc Computational Statistics and Machine Learning Project (COMP0098) (60 credits)
Students must choose 45 to 75 credits from the available optional modules. Students must take either Graphical Models or Probabilistic and Unsupervised Learning.
- Graphical Models (COMP0080) (15 credits)
- Applied Machine Learning (COMP0081) (15 credits)
- Advanced Topics in Machine Learning (COMP0083) (15 credits)
- Information Retrieval and Data Mining (COMP0084) (15 credits)
- Approximate Inference and Learning in Probabilistic Models (COMP0085) (15 credits)
- Probabilistic and Unsupervised Learning (COMP0086) (15 credits)
- Statistical Natural Language Processing (COMP0087) (15 credits)
- Advanced Deep Learning and Reinforcement Learning (COMP0089) (15 credits)
- Introduction to Deep Learning (COMP0090) (15 credits)
- Inverse Problems in Imaging (COMP0114) (15 credits)
- Multi-agent Artificial Intellgience (COMP0124) (15 credits)
- Machine Vision (COMP0137) (15 credits)
- Selected Topics in Statistics (STAT0017) (15 credits)
- Financial Engineering (COMP0048) (15 credits)
- Affective Computing and Human-Robot Interaction (COMP0053) (15 credits)
- Bioinformatics (COMP0082) (15 credits)
- Computational Modelling for Biomedical Imaging (COMP0118) (15 credits)
- Numerical Optimisation (COMP0120) (15 credits)
- Robotic Systems Engineering (COMP0127) (15 credits)
- Robot Vision and Navigation (COMP0130) (15 credits)
- Statistical Inference (STAT0008) (15 credits)
- Stochastic Methods in Finance (STAT0013) (15 credits)
- Stochastic Methods in Finance II (STAT0018) (15 credits)
- Statistical Design of Investigations (STAT0029) (15 credits)
- Statistical Computing (STAT0030) (15 credits)
- Applied Bayesian Methods (STAT0031) (15 credits)
Please note: the availability and delivery of optional and elective modules may vary, depending on your selection.
All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.