start with a problem definition for regular supervised learning and cast it in a probabilistic framework so a lot of what I'm gonna say some of you might have already seen may be in a course on machine learning or a textbook but I just want to walk through it step by step because the meta learning problem definition will build on this so if we're doing supervised learning what we're really doing is we're finding the most likely parameters Phi given our data D so Phi denotes the parameters of your model so if your training for example a