It's all about empowering people
You can think about machine learning as a three phrase process: preparation (definition, gathering, analysis, cleaning, selection), modelling (selection, development, evaluation) and implementation (evaluation, implementation). After implementation there should always be a way for the system to keep learning.
Update: He's currently going through an example using stock quotes, talking about clustering of data. I'm having flashbacks to yesterday's data mining tutorial. If you're interested in following along, apparently the code will be available for download after the talk.
Update: We've been pointed at "Knowledge-Based Clustering" by Witold Pedrycz as a good text on clustering algorithms. I think I've actually got this on my shelf at home, but I'm not sure I've ever done more than flip through it, I'll have to dig it out when I get back into the UK.
Update: Apparently he's a PDL and PGPLOT user. I'm feeling right at home here, this guy is definitely a an academic...
Update: His next (and final) example is based on medical diagnosis, and he's talking about support vector machines classifiers and his PDL implementation of the technique. I'm going to have to mail him at the end of this and get the source code to this, or persuade him to tidy it up and release it onto CPAN.
Update: If you want to code and the slides from this talk you need to email the speaker at firstname.lastname@example.org.