About This Course
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.
PhD: 3 years full-time; MPhil: 2 years full-time
International Year Zero: HSD GPA 2.0 OR SAT 1500+
International Year 1: HSD GPA 3.0 OR SAT 500+ each section OR ACTs 26+
Undergraduate Courses: HSD GPA 3.0 OR SAT 550+ each section OR ACTs 26+
Postgraduate Courses: Bachelor Degree GPA of 3.0. GRE not required
PhD/Research Course: Masters Degree
Note: Some courses may require higher entry requirements. Refer Individual Course page for details.
Applicants from USA need NOT provide additional evidence of English Language ability, if previous education was with English medium of instruction. Otherwise, an IELTS overall 6.0 with 5.5 in each component or equivalent is normally required (some courses may require a higher score).
A good honours degree or equivalent is required.