Reddit mentions of CUDA for Engineers: An Introduction to High-Performance Parallel Computing

Look at using the Eigen library for linear algebra in C++. Its used extensively in CV and AI settings, so there is a lot of info floating about it and lots of examples. It does take some getting used to coming from MATLAB though.

Here are some C++ books that have proven useful to me:
The Bible,
Very Useful,
My favorite data structures book,
[Maybe of interest] (https://www.amazon.com/Bundle-Algorithms-Parts-1-5-Fundamentals/dp/020172684X/ref=sr_1_25?ie=UTF8&qid=1484332390&sr=8-25&keywords=data+structures+in+C%2B%2B)

Also, keep in mind that the C++17 standard should be released this year, and there will be a new deluge of books.

Probably want to learn something about numerical analysis:
Numerical analysis

For vehicle dynamics and propulsion, are you thinking more FEA and CFD? If so, learning about GPU programming is probably more interesting since there is so much parallelization...
I recently picked this up but havent really worked through it yet...
but keep your expectations low, it is definitely non-trivial to try to spin your own packages, and it might be more worth your while to look at integrating with something like OpenFOAM for CFD, or to look into some of these packages for FEA. There are a lot of people who have spent a long time making these sorts of tools.

Alright, how about trying some CUDa, openGL, openCL, MPI etc parallel programming, e.g. http://heather.cs.ucdavis.edu/~matloff/158/PLN/ParProcBook.pdf

and https://www.amazon.com/CUDA-Engineers-Introduction-High-Performance-Computing/dp/013417741X

There's lots of code out there, you can modify it for performance tuning or algorithmic stability/correctness, you can run it on CPU and GPU to compare, etc. A lot of people are running machine learning programs without a full grasp of the math and getting results. You can read their stories here: http://blog.kaggle.com/category/winners-interviews/