CS188 Intro to AI – Course Materials
ai.berkeley.edu
ai.berkeley.edu
http://ocw.mit.edu/courses/electrical-engineering-and-comput...
6.034 emphasizes intuition over math, which might make it an easier alternative for those without a stats or calculus background. Also Prof. Winston is a phenomenal speaker.
Symbol: star is the symbol of this method, it makes the idea visual and memorable
Slogan: With these 5-star tips, you can 5x the impact of your good ideas
Surprise: You can achieve fame and impact by packaging your ideas better following these simple presentation tips
Salient: Having a good presentation is as important as having good ideas/work
Story: These presentation tips are told by a top MIT prof to his undergraduate AI class as secrets to career success
In one of his lectures there he pointed out that research papers with a single good idea are more likely to succeed (be cited and discussed) than a paper with multiple good ideas. After thinking about it for a while, I realized that this was the reason some of my personal projects failed to gain traction. It's a frustrating realization, but at least he has some concrete, easy to implement advice on how to overcome this issue.
His comments on the history of AI field are also very interesting.
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.
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.
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.
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.
Correct me if I'm wrong but I actually think the reason LISP was created was for AI.
Your link took me to an internal page.
http://rll.berkeley.edu/deeprlcourse/
And you should be well on your way to being able to read and comprehend the Google DeepMind papers posted on Arxiv ;)
From the knowledge gained from the MOOC, later on I made this game http://kenrick95.github.io/c4/demo/ which I implemented a simple AI as the opponent, which is a minimax agent. It is not perfect, but it is good enough for me :)
As it is, the class just skims over a variety of techniques and ways to implement them, lacking depth (such as what disadvantages and advantages there are in practice or where they have been used in the real world).
I tough a similar course for a while and they are doing a much better job than I did.
iOS 8.0 and greater