This is an awesome resource, and exactly what I've been wanting!
I've been thinking about AI a lot lately, but I skipped college and went straight into startups, so all my knowledge on the subject is from reading less focused materials. Reminds me of Stanford's iOS dev resources from 4-5 years ago.
[EDIT] oh god math, why can I program but I can't math unless it's trigonometry or vectors/calculus, visually applied math makes sense, otherwise I'm so lost.
Try to think of the math as weird notation for code.
For me, it really helped to be able to think of mathematical ideas in terms of code–as abstractions I could define and use in programs. I got this view by learning and using Haskell, and I do think Haskell is better-suited for this than other languages, but it's applicable to anything: think about how you would phrase the relevant math in your code, using whatever abstraction facilities you're comfortable with.
I actually took this class a few years back. There is certainly some math, but it's all math that's transferable to code in a reasonably natural way. (In fact, that's roughly what the small projects/homework assignments entail!) Doing the assignments while paying attention to the abstractions you use in your code is going to be a great way to get over the math hurdle.
> Reminds me of Stanford's iOS dev resources from 4-5 years ago.
Believe it or not, they still release a course every year (or semester, not sure). The latest one covers iOS 9 using Swift. You can find them all on iTunes U.
Try this. Choose a lecture. As you go through it, create a bulleted list of things you don't know. A Google Doc works well.
Then go through the bulleted list in order. During this process, you will also encounter things you don't know. Add these to the top of the list and start from the top anew. As you work through bulleted items, mark them off or move them to a "complete" list.
What you are creating is a list of things you need to understand in the order you need to understand them. It guides your investigation and makes it all more manageable.
Bonus: Beneath the list, create an in-order set of notes pertaining to the items on your list. This is like a personalized set of lecture notes.
Take a functional programming class. Martin Odersky on Coursera now has a while track. It's Scale. If that matters. Functional programming is the blood-brain barrier between programming and math with lots of activity bringing the two closer. It may not be directly useful in Machine Learning type of math, but it's a strong foundation for parallel programming and multi-core programming due to the statelessness of pure functions. Good luck!
From personal experience, a functional programming style helps one to reason with the math used in artificial intelligence. This might be why lisp is considered one of the original AI languages. The power of being able to express the networks as purely lists of numbers is amazing in my opinion.
Correct me if I'm wrong but I actually think the reason LISP was created was for AI.
I've been thinking about AI a lot lately, but I skipped college and went straight into startups, so all my knowledge on the subject is from reading less focused materials. Reminds me of Stanford's iOS dev resources from 4-5 years ago.
[EDIT] oh god math, why can I program but I can't math unless it's trigonometry or vectors/calculus, visually applied math makes sense, otherwise I'm so lost.