Is the linear algebra of machine learning more complicated than that of graphics?
The linear algebra used in the basic raster pipeline to manage drawing a 3d unshaded mesh is pretty simple, and you can get by knowing just a little bit of linear algebra, like dot products and how to multiply matrices, and maybe what homogeneous coordinates are. But that is by no means the extent of linear algebra in all of graphics.
The linear algebra used in a basic neural network is also pretty simple, and you can get by knowing dot products and matrix multiply if you’re writing your own inference, and maybe just a tiny bit of derivative calculus if you’re writing your own backprop, but otherwise you don’t need anything else.
Students in both fields have to learn some basic linear algebra, but most people working in ML & graphics generally don’t use any linear algebra day to day, because most people aren’t writing inference/backprop and most people aren’t writing the graphics pipeline.
BTW, matrices and linear algebra are pure convenience for neural networks, and maybe for the graphics raster pipeline too. You can do both of these things without using matrices per se (though you might re-invent something equivalent and/or less efficient by avoiding matrices).