Speaker
Christopher Rogan
(The University of Kansas (US))
Description
This lecture will cover the relationship between "models" and "data", such that "learning" corresponds to making inferences about the parameters of our model. We will see that this paradigm covers everything from curve fitting to contemporary machine learning with neural networks. The theory of general model fitting will be explored with practical examples, with some discussion of the concepts of "goodness-of-fit" and model selection.
Author
Christopher Rogan
(The University of Kansas (US))
