[fizixer]

I would make a stronger statement: numerical algorithms (computational continuous mathematics) are seriously neglected in favor of combinatorial algorithms (computational discrete mathematics).

Take any undergrad algorithms class: 99% of it is combinatorial. There's an equal (probably larger) parallel universe of numerical thinking and numerical algorithms which is either not taught, or taught as a secondary class under the names of 'numerical analysis', 'scientific computing', and so on. None of stacks, queues, trees, and graphs are going to help you out when you have to discretize a differential equation or solve the resulting linear system with correctly enforcing the boundary conditions. You could ace an algorithms class and not have a clue how to begin tackling those kinds of problems.

This is especially relevant today when machine learning is heavily dependent on computational linear algebra and other numerics, and a typical CS student is not trained for that.

[graycat]

> a typical CS student is not trained for that.

And what about a typical CS prof? No, I won't name any names, not today.