The 2017/18 DRPS Course Descriptor provides an overview of the course and its requirements. Further have a look at the textbook and the lecture slides of the first lectures. They should give you an idea about the course.
The course covers foundational material in machine learning and provides you with tools and skills to understand many different methods. But this also means that the course is generally more theoretical and mathematical than the other machine learning courses at Informatics (e.g. IAML, MLP, or MLPR). It will feature more pen and paper work than programming and is not an applied course.
If you took MLPR and found the math and theory aspects difficult or not interesting, you will likely not enjoy PMR. You might want to have a look at the more applied DME instead.
If you didn’t take MLPR, please take the MLPR self-test. If you struggle with the test, you will risk struggling with PMR too.