detail so they will switch to sometimes the second part of the course first part of the course was all about finding optimal actions how do we given a dynamical system some environment really find a sequence of actions or a policy that optimizes expected reward now we're going to look at the complementary part which is trying to make sense of our sensor readings such that we actually even know what the state of the environment might be and then act against that and we'll need a lot more probability here so we'll start with a