Linear Algebra is like a lot of math in CS; you don't necessarily see it initially, but once you get some familiarity you start seeing it everywhere. Others ha

Linear Algebra is like a lot of math in CS; you don't necessarily see it initially, but once you get some familiarity you start seeing it everywhere. Others have commented on computer graphics, but (as it turns out) the exact same algorithms apply for Collision Detection in 3D space. And since games already are manipulating graphics, you add on another set of Linear Algebra transforms that change the position/rotation/shear of those vertices. In a similar way, science (especially physics) use linear algebra to build simulations of all kinds of systems. One surprising use is in Advertising (and other user preference aggregators). Turns out a preference acts like a magnitude of a one-dimensional vector. String N preference vectors together and you get an N-Dimension Vector that you can perform Linear Algebra operations upon. One common application is the Dot Product, which is a fancy way of taking two N-Dimension vectors and measuring how close those vectors point in the same direction in a [1, -1] range. Yet another common place to find Linear Algebra is in computer science papers. Most of the time this is simply notation; a lot of common programming forms can be represented by MxN matrices. However some of those algorithms will use LA as a way of parsing and breaking down a problem. You will see this in compiler papers often, but its transferable to many other domains. As a final and personal observation, I found that Linear Algebra helped me grasp Functional Programming. In both cases I am applying a transform to some input, and often stringing together many such transforms. Also in both cases, the transformations are sensitive to their order, and a bad ordering produces nonsense just like garbage data.