ABSTRACT:

Because uncertainty, data, and inference play a fundamental 
role in the design of systems that learn, probabilistic 
modelling has become one of the cornerstones of the field 
of machine learning.  Bayesian methods, in particular, 
describe how probabilities can be used to coherently represent 
the degrees of belief of a rational agent. Bayesian methods 
work best when they are applied to models that are flexible 
enough to capture the complexity of real-world data. Recent 
work on non-parametric Bayesian machine learning provides 
this flexibility. I will touch upon key developments in the 
field, including Gaussian processes, Dirichlet processes, and 
the Indian buffet process (IBP). Focusing on the IBP, I will 
describe how this can be used in a number of applications such 
as collaborative filtering, bioinformatics, cognitive modelling, 
independent components analysis, time series modelling, and 
causal discovery. Finally, I will outline the main challenges 
in the field: how to develop new models, new fast inference 
algorithms, and compelling applications.