I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out but too dumb for traditional grad school.
What did you learn so far about neural nets? I recently looked into machine learning and naively thought I could find at least one practical fun tutorial "Here is a neural network API in C, you have to do that and this to let a simulated robot evade obstacles or learn to play Asteroids". Instead my (extremely superficial) search did find that neural nets are pretty arcane, genetic algorithms trapped in local minima and you are faster and better of coding logic yourself, developing a mathematical model to calculate results, instead searching for patterns in vast sets of data.
SVMs are a relatively easy to use (but not to understand) method that yield impressive results for beginners. See the libsvm website, there are plenty of good material there (http://www.csie.ntu.edu.tw/~cjlin/libsvm/).
But as stated somewhere else here, game AI is a whole different story.
If you are trying to make game AI then neural networks are a bad idea. But if you are just trying to have some fun learning then do whatever you want. That said, MLPs are hard to use properly, not a good place to start.
A good way to start "AI". Write a decision tree they will serve you well and with boosting do even better. Basic but useful stuff: logistic regression, armed bandits, weighted experts, kNearest, k means , Kernel Density estimation and Naive Bayes. That covers online, ensemble, super and unsuper vised algorithms. Goodluck!
I learned that neural nets research requires serious math knowledge, so that's what I'm working on currently.
One fun project to start learning about NNs is a balancing pole problem. There are solutions in various languages.
Yes, for some problems it might be faster and better to code logic yourself, but there are also tasks (such as pattern recognition) where NNs might be more effective.
You need at least probability/statistics, linear algebra, calculus, and numerical methods. Once you know this machine learning is a relatively thin layer built on this math foundation. The real problem is learning the math foundation.
It might be enough to be able to use tools that other people have built, and have a rough idea of what's going on under the hood, and how to select which algorithm to use in a very general sense. It won't be enough for you to be designing your own algorithms.
