The course introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain.
Core modules: supervised learning; either graphical models or probabilistic and unsupervised Learning. Options: machine vision; bioinformatics; information retrieval and data mining; advanced topics in machine learning; inverse problems in imaging; affective computing and human'robot interaction; approximate inference and learning in probabilistic models; applied machine learning; computational modelling for biomedical imaging; programming and mathematical methods for machine learning; statistical natural language programming.
Course Additional Entry
A minimum of an upper 2nd Class UK Honours degree in a highly quantitative subject such as computer science, mathematics, electrical engineering or the physical sciences, or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. Additionally, candidates must be comfortable with undergraduate mathematics in areas such as linear algebra and calculus.
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