Use of neural network, both deep and shallow, require learning a large number of parameters (weights) from observed data. Due to limited amounts of data there is a danger of overfitting. Further, the amount of uncertainty in the estimated parameters can have huge effect on the prediction performance. In this seminar we will discuss the Bayesian approach to learning and fitting neural networks. Both benefits of such approaches and conceptual as well as computational challenges will be discussed.
The lecture will be held by Prof. Geir Storvik, UiO.