Computational Statistics and Machine Learning MSc
Postgraduate
In London
Description
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Type
Postgraduate
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Location
London
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Duration
1 Year
This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.
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About this course
There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include: finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.
A minimum of an upper second-class UK Bachelor's degree in a highly quantitative subject such as computer science, statistics, mathematics, electrical engineering or the physical sciences, or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. Students must be comfortable with undergraduate-level mathematics; in particular it is essential that the candidate will have knowledge of statistics at an intermediate undergraduate level. The candidate should also be proficient in linear algebra and multivariable calculus.
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Subjects
- Computational
- Supervised Learning
- Data analysis
- Analysis of data
- Analysis and Design
- Analysis procedures
- Reinforcement Learning
- Machine Learning
- Machine Vision
- Natural Language
- Language delays
Course programme
The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.
Students undertake modules to the value of 180 credits.
The programme consists of two core modules (30 credits), four to six optional modules (60 to 90 credits), up to two elective modules (up to 30 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.
Core modules- Supervised Learning (15 credits)
- Statistical Modelling and Data Analysis (15 credits)
Students must choose 15 credits from Group One Options. Of the remaining credits, students must choose a minimum of 30 and a maximum of 60 from Group Two, 15 credits from Group Three and a maximum of 30 credits from Electives.
- Group One Options (15 credits)
- Graphical Models (15 credits)
- Probabilistic and Unsupervised Learning (15 credits)
- Group Two Options (30 to 60 credits)
- Advanced Deep Learning and Reinforcement Learning (15 credits)
- Advanced Topics in Machine Learning (15 credits)
- Applied Machine Learning (15 credits)
- Approximate Inference and Learning in Probabilistic Models (15 credits)
- Information Retrieval and Data Mining (15 credits)
- Introduction to Deep Learning (15 credits)
- Machine Vision (15 credits)
- Statistical Natural Language Processing (15 credits)
- Group Three Options (15 credits)
- Applied Bayesian Methods (15 credits)
- Statistical Design of Investigations (15 credits)
- Statistical Inference (15 credits)
Please note: the availability and delivery of optional modules may vary, depending on your selection.
Dissertation/reportAll MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.
Teaching and learningThe programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.
Additional information
Computational Statistics and Machine Learning MSc