Where to after Machine Learning?

[johnh]

I know there's a ways to go yet, but we can see the light at the end of the course. What to do next? Everyone seems to be interested in something hands-on. I don't have a specific topic, but here are two suggestions for general direction:

• Big data. The inspirations for this are the final ML lecture and Nathan Yao's book Visualize This! and the website flowingdata.com. It most likely leads off in the direction of the R language, which could be a nice tool to complement Octave/MatLab. 
• Robotics. The inspirations are facial recognition / 'eigenfaces' and a recent NPR story on Willow Garage, a robotics outfit offering pricey robots and open-source software. This leads off towards ROS (Willow's OS for robotics), OpenCV, and Point Cloud Library (PCL). 

Either of these could provide a lot of learning opportunities and plenty of fun.

[johnh]

(Under robotics, I meant to include mention of the book Probabilistic Robotics by Sebastian Thrun, along with Wolfram Burgard and Dieter Fox.)

[Grant Rettke]

Find a company here in town with big data that needs some help and
make a project out of it.

--
http://www.wisdomandwonder.com/
ACM, AMA, COG, IEEE

[Dave Colwell]

I think I started to salivate a little while reading the topics you proposed.  Both tracks sound like a lot of fun!  Hope to see you all at the coffees shop tomorrow to discuss further.

[Dave Colwell]

     Today we brainstormed ideas for "what's next."  Everyone seems pretty excited about the many potentials with visual recognition.  We're going to each do some research and put together a project proposal to share next time.  We also discussed the Leap Motion input device.