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.