I'm a neuroscientist in a doctoral program but I have a growing interest in deep learning methods (e.g., http://deeplearning.net/ ). As a neuroscientist using MR imaging methods, I often rely on tools to help me classify and define brain structures and functional activations. Some of the most advanced tools for image segmentation are being innovated using magical-sounding terms like Adaboosted weak-learners, auto-encoders, Support Vector Machines, and the like.
While I do not have the time to become a computer-science expert in artificial intelligence methods, I would like to establish a basic skill level in the application of some of these methods. Soylenters, "Do I need to know the mathematical foundation of these methods intimately to be able to employ them effectively or intelligently?" and "What would be a good way of becoming more familiar with these methods, given my circumstances?"
(Score: 0) by Anonymous Coward on Wednesday July 01 2015, @12:47AM
I think you are wrong to ignore the distinction. Using your vague definition of "artificial intelligence" would allow any kind of updating procedure to be classed as such. People would not call gradient decent or mcmc examples of intelligence. They are just algorithms that compare different models to data and choose some as "best" according to some preset criteria.