Help needed with literature survey ...

Do you have time to help with surveying the literature on machine learning algorithms and datasets in respect to preparations for creating cognitive demos for learning from examples? 

The aim is to develop a series of demos focusing on techniques for learning knowledge graphs and decision trees from potentially noisy examples, using strongly supervised, weakly supervised, and unsupervised learning algorithms based on metrics for parsimonious representations. Humans are able to learn from small numbers of examples in contrast to today’s deep learning techniques. This is possible through effective use of statistics and prior knowledge. See:

 https://github.com/w3c/cogai/blob/master/demos/README.md <https://github.com/w3c/cogai/blob/master/demos/README.md>

As you can see, I have added a small set of references to get us started. The idea is to start with a simple demo and to then work on progressively more ambitious challenges.  The demos should be based upon cognitively plausible techniques involving graph data, graph rules and graph algorithms.

p.s. you are also encouraged to introduce yourself and your interests on this list so that we can start to build an effective community.

Many thanks,

Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
W3C Data Activity Lead & W3C champion for the Web of things