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I did something similar for my capstone, and my recommendation is that you start a project. I also started by reading lots of papers from the ACM DL, reading bo
by arebop 16y ago
I did something similar for my capstone, and my recommendation is that you start a project. I also started by reading lots of papers from the ACM DL, reading books from my school library, etc. If I could do it over again, I'd start programming earlier.
It sounds like you're trying to learn about AI before you work on applying anytime classifiers in an application. My suggestion is that you interleave the studying with working, so that you can do directed study rather than breadth-first traversal through a huge subfield in CS.
Start with a very modest goal for classification and work on building a neural network for that. Guess about which particular algorithm and feature vector structure to use. Work to improve your results for a while, then go back to reading in order to pick up a new technique to plug in. Consider setting a more challenging goal. Iterate on that.
- Groxx 16y agoI have a large part of a project finished already, actually. I built a graph-based hexagonal tile system + Settlers of Cataan rules, with the explicit goal of writing computer opponents for it. I've had a neural networks course, and have written a couple for small tasks, as well as a couple genetic algorithms. What I'm interested in now is learning more of the theories underpinning things. A longer-term goal is to learn NLP + bayesian filters, and to start fiddling around with email / content classification. It seems the sort of thing I shouldn't have to do 90% of the time, so it'd be nice to automate it.