learning problem then we'll be talking about different meta learning algorithms ranging from black box out updation approaches to autumn tape optimization based approaches to nonparametric methods then we'll discuss how we can develop bayesian variants of each of these methods then we'll talk about how meta learning has a plot been applied to different application areas we'll take a short five-minute break and also allows additional questions Sergey will then talk about meta reinforcement learning and we'll conclude by discussing