This course provides an in-depth study of statistical machine learning approaches. The focus will be on methods for learning and inference in structured probabilistic models, with a healthy balance of theory and practice. It is aimed at postgraduate students and advanced undergraduates who are willing to go beyond basic understanding of machine learning.

The course provides fundamental support for those willing to intensify their knowledge in the area of big data analytics. It will cover topics on exact and approximate inference in probabilistic graphical models, learning in structured latent variable models, and posterior inference in non-parametric models based on Gaussian processes.