Reddit reviews Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
We found 2 Reddit comments about Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics. Here are the top ones, ranked by their Reddit score.
Your university will let you take as many classes as you want.
You aren't in university to complete a checklist and move on. Take all the classes which will give you marketable skills.
PDE is pretty hit or miss. Almost every differential equation is analytically unsolvable. In most undergraduate ODE and PDE courses you will be wasting your time practicing how to solve one of a very small number of special cases by hand, without learning the deeper theory (because frankly, you're not ready to).
Yes, really; the way most everywhere teaches DE is useless and "done for historical reasons". I think they do it just because it makes their job (turning freshmen into grad students) easier by letting you practice calculus for a while.
At best you learn how to use integral transforms (Laplace transform, maybe with a mention to the Fourier transform) and maybe some connection to linear algebra (it might be the first time you see linearity used outside of a 'straight line' context).
In reality you will almost always need numerical techniques when it comes to differential equations. Those come in all shapes and sizes. Learn numerical methods. I like this book. There is a pdf out there.
For your general erudition it is important to understand the derivation of the heat equation in terms of a Fourier series. The heat equation is just a special case of the diffusion equation, and the diffusion equation is everywhere.
PDEs are used in computer vision. Of course they are. But you can probably skip the class itself unless you happen to become interested in one of the fields where they play a more central role. For most of computer vision my advice is to focus on these things:
PS: Studying abroad is worth the cost, even if you have to delay graduation and pay for it with student loans.
This is a good resource:
https://www.amazon.ca/Numerical-Algorithms-Computer-Learning-Graphics/dp/1482251884
Linear Algebra by Shilov is also good, but a little hard.
Also, please never write the words "Linear Algebraic Calculus" ever again.