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MSc Computational Statistics and Machine Learning

  • DeadlineStudy Details: 1 year

Masters Degree Description

Enhance your expertise in machine learning and statistics with one of the most established Master's programmes in this field. Our one-year Computational Statistics and Machine Learning MSc combines essential knowledge from both subjects, preparing you to excel in a data-rich world. With opportunities to study modules in collaboration with the prestigious Gatsby Computational Neuroscience Unit and Google DeepMind, this MSc is an exceptional opportunity to build your skills and expertise.

Entry Requirements

A minimum of an upper second-class UK Bachelor's degree (or international qualification of an equivalent standard) in a highly quantitative subject such as computer science, mathematics, electrical engineering, or physicals sciences. Additionally, applicants must be comfortable with undergraduate level mathematics, in particular statistics at an intermediate undergraduate level, and be proficient in linear algebra and multivariable calculus. Relevant work experience may also be considered.

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Fees

For fees and funding options, please visit website to find out more.

Programme Funding

UCL offers a range of financial awards aimed at assisting both prospective and current students with their studies.

Student Destinations

Graduates from the Computational Statistics and Machine Learning MSc have been employed at major tech and finance companies. Graduate Outcomes survey data shows graduates going on to roles at Amazon, Apple, McKinsey, Microsoft and UBS, among others. Others have pursued further study, for example, at the universities of Cambridge, Helsinki, Chicago, and UCL, or gone on to a career in academia.

Module Details

Compulsory modules

  • Supervised Learning
  • MSc Computational Statistics and Machine Learning Project
  • Statistical Models and Data Analysis
  • Graphical Models
  • Probabilistic and Unsupervised Learning
  • Open-Endedness and General Intelligence
  • Applied Machine Learning
  • Advanced Topics in Machine Learning
  • Approximate Inference and Learning in Probabilistic Models
  • Statistical Natural Language Processing
  • Reinforcement Learning
  • Machine Vision
  • Bayesian Deep Learning
  • Applied Deep Learning

Optional modules

  • Graphical Models
  • Probabilistic and Unsupervised Learning
  • Open-Endedness and General Intelligence
  • Applied Machine Learning
  • Advanced Topics in Machine Learning
  • Approximate Inference and Learning in Probabilistic Models
  • Statistical Natural Language Processing
  • Reinforcement Learning
  • Machine Vision
  • Bayesian Deep Learning
  • Applied Deep Learning
  • reasonable adjustments
  • Student Support and Wellbeing Services
  • UCL Student Support and Wellbeing Services
  • AccessAble
  • UCL Student Support and Wellbeing Services.

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