I'd say yes. You could bring Support Vector Machines [0], bring the Vapnik-Chervonenkis theory of statistical learning [1]. You could fast forward the whole fie

I'd say yes. You could bring Support Vector Machines [0], bring the Vapnik-Chervonenkis theory of statistical learning [1]. You could fast forward the whole field of Reinforcement Learning, you would be able to show that it's possible to solve problems like passing gradients through a sampling process using the reparameterization trick [2], you would be able to show that you can pass gradients through a sorting process [3]. You would also have experience of working with autodiff software and build such. Imho the advent of autograd, tf, torch and so on helped tremendously in accelerating progress and research because the focus of development is not on correctness anymore. [0] https://en.wikipedia.org/wiki/Support-vector_machine https://en.wikipedia.org/wiki/Support-vector_machine [1] https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_theory https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_th... [2] https://openreview.net/forum?id=33X9fd2-9FyZd https://openreview.net/forum?id=33X9fd2-9FyZd [3] https://old.reddit.com/r/MachineLearning/comments/mcdoxs/p_torchsort_fast_differentiable_sorting_and https://old.reddit.com/r/MachineLearning/comments/mcdoxs/p_t...