模式识别是一个非常活跃的研究领域，与机器学习和数据挖掘紧密相关。模式识别也称为分类或统计分类，旨在建立可以确定输入模式类别的分类器。输入内容可以是信封上的邮政编码，卫星图像，微阵列基因表达数据，油田探针的化学签名，公司的财务记录等等。分类器可以采用函数，算法，一组规则等形式。模式识别是关于训练此类分类器执行可能对人类而言可能是乏味，危险，不可行，不切实际，昂贵或简单的任务。模式识别在海量数据收集的现代时代（例如在零售，通信和Internet中）以及对精度和速度的高要求（例如在安全监控和目标跟踪中）面临许多
Pattern recognition is a very active field of research intimately bound to machine learning and data mining. Also known as classification or statistical classification, pattern recognition aims at building a classifier that can determine the class of an input pattern. An input could be the ZIP code on an envelope, a satellite image, microarray gene expression data, a chemical signature of an oil-field probe, a financial record of a company and many more. The classifier may take a form of a function, an algorithm, a set of rules, etc. Pattern recognition is about training such classifiers to do tasks that could be tedious, dangerous, infeasible, impractical, expensive or simply difficult for humans. Pattern recognition faces many challenges in the modern era of massive data collection (e.g. in retail, communication and Internet) and high demand for precision and speed (e.g. in security monitoring and target tracking). New methodologies are needed to answer these application-born challenges.<br><br>Programme Length - PhD: 3 years full-time, MPhil: 2 years full-time