I am really enjoying spending more time indulging in some good old fashioned Computer Science. Getting back to theory and basics is a great reminder of what I love about computers, what they can do, what they can be.
Machine learning and associated AI topics are attracting a lot of interest these days (much of it warranted, some of it not) but every so often you need some good old fashioned linear algebra.
This paper [PDF] uses the academic equivalent of link baiting with a provocative title for what is really an applied discussion of linear algebra, using Larry & Sergei’s PageRank algorithm (or at least a simplified, public domain version of it.)
I often struggled in college to connect the abstraction of theory to its practical application AKA “how will I ever use this in the real world?”, so I love papers like this that try to connect the dots more. (You really need a discussion of Markov chains here too for the full picture, but thats another paper I guess.)