Machine learning methods are being increasingly and successfully applied to many different physics problems. However, current machine learning approaches do not model uncertainties well - if at all. In this talk I will discuss how using Bayesian neural networks can give us a handle on uncertainties in machine learning. I will use tagging top quark vs. light quark and gluon jets as an example of how these networks are competitive with other neural network taggers with the advantage of providing an event-by-event uncertainty on the classification. I will then further discuss how this uncertainty changes with experimental systematic effects, using pile-up and jet energy scale as examples.