Data science brings together computational and statistical skills for data'driven problem solving. This rapidly expanding area includes machine learning, deep learning, large'scale data analysis and has applications in e'commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence. The courseprovides a principled understanding of the computational and statistical underpinnings of current and emerging methods.
Information retrieval and data mining; introduction to statistical data science; applied machine learning; introduction to supervised learning; machine vision; cloud computing; web economics; statistical natural language processing; programming and mathematical methods for machine learning; financial data and statistics; statistical models and data analysis; applied Bayesian methods; decision and risk; data analytics; supervised learning; graphical models; bioinformatics; affective computing and human'robot interaction; computational modelling for biomedical imaging; stochastic systems; forecasting.
Course Additional Entry
An upper 2nd Class Bachelor's degree (or equivalent overseas qualification) in a quantitative discipline (such as mathematics, computer science, engineering, physics or statistics) from a UK university or an overseas, qualification of an equivalent standard. Knowledge of mathematical methods including linear algebra and calculus at first'year university level is required.
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