Handbook

Machine learning aims to develop computer systems that learn from example data to model and solve real-life problems. Students will develop the knowledge and skills required to analyse, design and implement machine learning systems applicable in big data analytics, social media data analysis, computer vision, neuroimage analysis, speech recognition, surveillance, information retrieval, bioinformatics, document image analysis and recognition, computational linguistics, forensics and biometrics. Conceptual understanding of the fundamental tools and their application in practice will be emphasised. Topics covered include: supervised and unsupervised learning; kernel machines; deep learning; data clustering; Bayesian methods; linear discriminant analysis; regression; and graphical models.