At work, we’re at a point where we’ve come into possession of a huge volume of data – data that is just begging to be sliced and diced, with the promise of unveiling secrets that lay unbeknownst to us for now.

My brief fling with Machine Learning along with conversations with a respected colleague led me to explore Singular Value Decomposition (SVD). The application of this technique supposedly played a significant role helping team “BellKorâ€™s Pragmatic Chaos” take home the Netflix Prize.

So I started at the wikipedia page for SVD and found myself clueless as soon as the first equation appeared. No worries. Taking a step back, I find out that SVD is a factorization technique within a branch of mathematics called Linear Algebra.

Linear Algrebra it shall be.

My daily train commutes, where possible, have found me following the MIT lectures of one sufficiently old and distinguished Gilbert Strang teaching Introduction to Linear Algebra. The lecturer I never had.

I’m also having a go at working through the course textbook Introduction to Linear Algebra and doing the chapter-end problem sets. So far, I’ve started to grasp vector arithmetics, and some cursory idea of computing matrices. As a bonus, the concept of “singular, un-inversible matrices” has emerged in Lecture 3.

Hopefully I’ll have this SVD thing down pat in due time.