Data arising from business transactions, scientific measurements and other forms of content-creation calls for automatic data mining and pattern recognition techniques that allow us to efficiently make sense of this data. At the same time these techniques should be able to handle uncertainty, as data from measurements may be imprecise and user-generated content may be unreliable.
This lecture will introduce the main concepts of data mining and probabilistic reasoning, ranging from basic probability and information theory to popular classification and clustering algorithms. An introduction to the exciting area of graphical models and probabilistic inference will highlight the link between uncertainty and probabilistic learning models.
