One of the things I miss most about the pandemic was how all of these institutions opened up for the world. Lately they have been closing down not only newer course offerings but also putting old videos private. Even MIT OCW falls apart once you get into some advanced graduate courses.

I understand that universities should prioritize their alumni, but there’s literally no cost in making the underlying material (especially lectures!) available on the internet. It delivers immense value to the world.

If that seems unlikely, remember that image generation didn’t take off till diffusion models, and GPTs didn’t take off till RLHF. If you’ve been around long enough it’ll seem obvious that this isn’t the final step. The challenge for you is, find the one that’s better.

spring course is on YouTube https://m.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpT...

Take, for example, a typical binary classifier with a BCE loss. Suppose I wanted to shoehorn RL onto this: how would I do that?

Or, for example, the House Value problem (given a set of features about a house for sale, predict its expected sale value). How would I slap RL onto that?

I guess my confusion comes from how the losses are hooked up. Traditional losses (BCE, RMSE, etc.) I know about; but how do you bring RL loss into problems?

I've already studied a lot of deep learning.

Please confirm if these resoruces are good, or suggest yours:

Sutton et al. - Reinforcement Learning

Kevin Patrick Murphy - Reinforcement Learning, an overview https://arxiv.org/abs/2412.05265

Sebastian Raschka (upcoming book)

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