But how's your knowledge of Lagrangian mechanics? Poisson distributions? Standard deviation? The rules of integration and differentiation? Pipelined architectures? Data structures? Computational efficiency? BN notation? Colour theory? The physics behind lenses? Mutexs? Locks? Lock-free code? SIMD? Solutions to sparse matrices? Can you dervie the equations to convert a quaternion to a matrix from scratch?
Half of that stuff, having a double major myself I dont even know. And dont see the point in knowing either.
Then you still have a lot to learn ;)
People often say that all you need to know are matrices, quats, trigonometry, and maybe some newtonian mechanics thrown in as well. Whilst you can 'get-by' using this stuff, knowledge of the aspects I listed above will be very helpful in the long run. As an example, consider statistics (that branch of mathematics everyone hates). I used to consider stats to be completely pointless, utterly useless, and hated the fact I was forced to learn various distributions at A-Level. Fast forward ten years, and I then realised that stats can provide a really useful tool when trying to compress data assets. If you know the mean of the data set, its standard deviation, and the distribution it follows; it's very easy to come up with a compression scheme that's tailored to your data set.
You might think that knowing how to derive the equations for matrices/quats/etc is completely pointless (because you can just find a bit of C code online that does the computation and use that). In reality however, you'll often need to make that code work with SSE/alti-vec/neon instruction sets. If you take a routine optimised for the FPU and try to fit it into SIMD, it usually ends up being less efficient than the original FPU methods. If however you understand how a formula was derived, you can usually find a solution (from first principles) that will fit the hardware better.
One of the first things you learn at university level mechanics, is that newtonian methods are very simple to understand, but they require a hell of a lot of computation. Transforming the problem into lagrangian mechanics give you a much more efficient (and numerically stable) way of computing dynamics simulations. If you ever need to look at the solver code for Havok or PhysX, this stuff will be immensely useful!
So you might consider all of those topics completely pointless. I however, consider them to be really useful tools to simplify and optimise some of the harder problems you'll find when developing computer games. All knoweldge you learn, will one day become invaluable - it's just very hard to see why that's the case when you're first learning a topic.
