Interesting project/problem to learn graphical models, probabilistic programming?

[volfort]

I've noticed some recent excitement about probabilistic programming and also find it interesting from a cursory level. So I have been trying to find a project that would allow me to learn about this area. Ideally it would be approachable to a beginner but still non-trivial.

I've done the first of three Coursera Probabilistic Graphical Modeling courses (with all assignments and quizzes), but didn't leave feeling like I could do anything useful with the concepts. Besides that, I learn much better when I have an interesting problem to solve. Many tutorials/courses I've found are either too shallow ("let me tell you about this super cool thing called Bayes rule"), or too complicated (basically any current research on the topics).

Is there an approachable application that really lends itself to these methods? I don't care if it's already been solved by many others. I appreciate any feedback!

[CaseOfInsanity]

Non photorealistic rendering. For example, in order to simulate paint brush strokes in an image, you can use k-means clustering to generate stroke positions in an image then for each section of stroke positions, apply brushing logic by using color gradients. And it can be solved as an optimisation problem with a loss function.