Stanford ML 2: Linear Algebra Review

Machine learning makes extensive usage of linear algebra, probability, and calculus. CS229 reviews basic linear algebra early on. If you're new to linear algebra, it's certainly worth spending time on; I use it extensively in my professional life.

I might expand on this subject more over time, but for now I would just highlight a few things:

  1. I used Gilbert Strang's text when I was first learning the subject in school, and it was honestly one of my favorite textbooks. I recommend both Introduction to Linear Algebra and Linear Algebra and Its Applications. Strang is a true teacher: he loves the subject, and is committed to making complicated ideas understandable. And all the video lectures for his "Linear Algebra" and "Computational Science and Engineering" classes at MIT are available on OpenCourseWare.
  2. You can find an introduction related to CS229 in Python with Numpy on Codebright's Blog.
  3. The best R introduction to Linear Algebra that I could find is "Linear algebra in R" by Søren Højsgaard. This covers all the material required for CS229.

Basic Linear Algebra in R

Here are some of the basic ideas covered in the CS229a lectures.

For now, I won't spend any more time on linear algebra because I presume most readers are already familiar and I'd rather commit that time to exploring the next topics: multivariate and logistic regression, and regularization.

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