This course 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, quantative finance, artificial intelligence and machine vision.
Core modules: supervised learning; statistical modelling and data analysis; graphical models or probabilistic and unsupervised learning. Plus one of: applied Bayesian methods; statistical design of investigations; statistical computing; statistical inference. Options: advanced topics in machine learning; affective computing and human'robot interaction; applied Bayesian methods; applied machine learning; approximate inference and learning in probabilistic models; bioinformatics; computational modelling for biomedical imaging; forecasting; information retrieval and data mining; inverse problems in imaging; machine vision; programming and mathematical methods for machine learning; selected topics in statistics; statistical computing; statistical design of investigations; statistical inference; statistical natural language programming; stochastic methods in finance; stochastic methods in finance 2.
Course Additional Entry
A minimum of an upper 2nd Class UK Honours 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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