Although I won't give you a full breakdown of my timetable here let me relate some more information on the lectures I am currently attending here at Brunel. Generally speaking, lectures over here are much more informal and example-oriented than those I heard in Hannover. The normal length of a lecture is 50 minutes and is given twice a week with an additional tutorial hours for comprehension and review. Although you are required to make notes in some of the modules most lectureres hand out a script which makes understanding a little harder (speaking for me) as you're easily losing track thinking about something completely different (e.g. what you're going to eat at the refectory).
Partial Differential Equations This is a tough one for me who never even liked the chunks of ordinary differential equations back in his UG years.
Ordinary Differential Equations Now this is more familar. Linear Multistep Methods (LMMs), Simpson's rule, cosistency of LMMs. We heard about that before, didn't we?
Dynamical Systems
This course is split up in two parts: one is intended for postgraduate students only, the other
addresses undergraduate students as well. No real difficulties here. The UG-parts deals with
discrete dynamical systems, these are simple iterations as X(n+1) = c*Xn*(1-Xn), which are
investigated to learn about fixed points, etc. The PG-parts transports this to a more advanced
level (here: from the discrete to the continuous). The first theorem proved is the Inverse Function
Theorem (also known as Implicit Function Theorem). So again, firm ground.
Linear Programming and Optimization
Now this is really Applied Mathematics. This course revolves around the Simplex Algorithm
that may be used to solve maximazation problems that occur, as you might guess, in industrial
applications. Again, this lecture addresses undergraduate students as well and runs at medium pace.
Dynamical Systems and Unconstrained Optimization
This course gives information on the various methods used in the numerical solution of equations
containing a single vector- or scalar-values function f(x) and minimizations problems for those
mappings as well. Hmmm, sounds familiar, doesn't it? We're talking about Newton's Methods and related
numerical methods here. Those who attended Numerics in Hannover will be informed about the foundations
of this lecture.