This course is meant for biologists who want to learn mathematical principles relevant to current biological research. About half of the course covers topics on information theory, inference, statistics, and probabilistic modeling. The second half of the course covers dynamical systems in biology, including random walks, feedback control, and molecular population dynamics.
Each week-long unit is devoted to one specific topic, and is based in one or more scientific papers selected from the recent literature. Each unit includes a set of lectures (available online), a practical session, and a homework. The practical session follows a flipped-class model in which students work in the classroom implementing the methods described in the lecture.
I will show you how to be critical with your data; and how to run the right control experiments. This course covers some of the mathematical tools to do that. I will use plain language as much as possible without losing mathematical rigor. I want to get across to you that you should not use statistical tests for which you do not understand the assumptions (and they all have them!), nor treat math as a black box. And if your calculus fails you and cannot find an analytic expression, then you can solve a prob