Day 4 of the workshop is again a machine learning centered day. Today we were introduced into the world of Gaussian Processes, and ML approach which is rooted in statistics and models data by looking at the averages of a distribution of functions…it is a function of functions. In contrast to most other ML approaches it is also very well suited for small data sets, which is why I had my eye on them already for quite some time. However, Gaussian Processes are not perfect and interestingly enough, their drawbacks and benefits seem quite complementary with the benefits and drawbacks to neural networks. Deep Gaussian Covariance Networks (DGCN) find their origin in this observation, and were designed with the idea of compensating the drawbacks of both approaches by combining them. The resulting approach is rather powerful and in contrast to any other ML approach: it does not have any hyper-parameter!!
Tomorrow, during the last day of the workshop, we will be using this DGCN to optimize our raspberry pink lacquers.
