CCNCS Seminar Details
Probabilistic Modelling, Machine Learning and the Information Revolution
|Date/Time:||Wednesday 7 March 2012, 3.30pm|
|Location:||Keynes Seminar Room 4|
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. Probabilistic models also make it possible to exploit the many opportunities arising from the recent explosion of large data stores that are now ubiquitous in the sciences, society and commerce. Once a probabilistic model is defined, Bayesian statistics (which used to be called "inverse probability") can be used to make inferences and predictions from the model. Bayesian methods also elucidate how probabilities can be used to coherently represent degrees of belief in a rational artificial 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 survey some of our recent work in this area, including new models for time series, covariances, graphs, deep networks, and other interesting structured data sets.