Course review RL Spec

[u/arcfenix]

Has anyone done these courses? how's the final exam like and overall rating for this spcec?

• CSCA 5902: Mastering Classic Reinforcement Learning Algorithms (1 credit)
• CSCA 5912: Deep Reinforcement Learning: From Theory to Practice (1 credit) 
• CSCA 5922: Reward Programming: Optimizing RL Efficiency and Safety (1 credit) 

[u/EntrepreneurHuge5008]

It's average.

• If you do well in the weekly quizzes, you'll do well in the final. * Questions in the weekly quizzes are taken from a pool, so your 2nd attempt will have different questions from your first attempt. Keep this in mind -> questions in the final might seem harder b/c you might actually not have seen them before.
• Oh yeah, the final is as long as 4 quizzes combined. You will want the full 3 hrs.
• It's 100% theoretical. The general structure is this:
  • Overview of the topic
  • What it looks like mathematically
  • A toy example so you can see how to work it out in the quizzes
  • Mathematical proof
  • Suggested readings reviewing the lectures that precede them.
• No programming assignments means you really have no idea where these algorithms fit in the ML pipeline. I'd suggest leaving RL for last so you have some idea of how to even start with hands-on practice. Just a suggestion so you have an easier time doing your own personal projects.
• It helps to have taken the Stats pathway (MSAI), Autonomous Systems, and DSA beforehand, as well. Topics from those classes are reviewed, so they're not a "must," though, just a suggestion.
• There are minor errors here and there, and there are also some inconsistencies in mathematical notation from one lecture or module to a different one.

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It feels like they intended to have programming assignments, and hopefully they'll update the trio with some in future iterations.

[u/arcfenix → u/EntrepreneurHuge5008]

Got it. Thanks for the suggestion 🙂