The information age is characterized by the abundance of data, lots of it. It comes from social networks, CCTV cameras, sensors and on-line retailers, and the main challenge is to extract useful information from the deluge of available data. As data is inherently noisy, carries a level of uncertainty and is of large scale one typically relies on computationally-efficient probabilistic methods.
Probabilistic modelling offers computationally-efficient optimization methods for model construction and for solving difficult problems, based on localized models. It also offers a range of methods for providing the most informative presentation of high-dimensional data.
This module introduces students to the main concepts of probabilistic modelling, inference techniques, the projection of probabilities onto graphs, creating complex models from simpler building blocks and the ability to infer values from localized and computationally efficient operations. More specifically it includes: