Thanks Eric (and Jeremy and Johno). The course details are a bit sparse on the sign-up site. What's the expected time commitment for the course over the 5 weeks? And how useful would the course be if you missed a few of the courses and had to catch up later?
Everything is recorded so it doesn't matter at all if you catch up later. Some people in the preview course didn't start until we finished! Obviously you'll get better interaction with the community if you're following along at the same time, but that's the only real issue.
I'd say budget a minimum 4 hours homework + 3-4 hours lesson watching time.
I came here to upvote this and add on some more resources. (I read the main comment and literally thought "reductions.")
John Langford of Microsoft Research (and formerly of Yahoo! Research) has some some extensive theoretical and practical work here. In particular as they apply to machine learning problems. Check out his page on this[1]. I'd also recommend checking out Vowpal Wabbit[2] which is essentially a working implementation of many of these reductions in ML.
I have a disclaimer. I'm not an expert in category theory at all. However, my primary background is in mathematics and I've "looked over the basics" as they say.
If you'll allow me to dodge the question just a little bit, I'd recommend that you dive in and see what it feels like to start learning it. The way I think that could proceed is to a) go find a book, lecture, video, or other resource you'd like to engage with, and b) start devouring it. If you run into conceptual difficulties, start looking online for ways to explain that difficult concept to yourself so that you can proceed.
Hopefully that isn't flippant, but I've found myself in similar situations and I wish I'd just gone and started in this direction.
To try to answer the core question, I think if you have a high school background in logic and the ability to think abstractly, that's the only real prerequisite for learning category theory. Many of the advanced concepts in category theory are really explanations for how concepts in algebra, analysis, etc (<- sub-fields of mathematics) are grounded in category theory. In this sense, I find that folks gain a better understanding of category theory because they'll start to see similar patterns in their respective parts of mathematics. So, what makes studying it difficult is that many of the examples of "what a category is" are from various parts of math. This can be a big hurdle, but the works of Spivak are very good about presenting just the necessary category theory as well as giving you examples that aren't purely mathematical. Check out "Category theory for scientists (Old version)"[1] or "Seven Sketches in Compositionality: An Invitation to Applied Category Theory"[2]. They're books that I've studied and I really like how much effort the author has put in to make it accessible to non-mathematicians.
Some parting thoughts.
- You can understand Haskell & friends without understanding category theory
- This StackExchange answer[3] has some other useful information
