📚 Resources

Here, we’ll provide links to other materials online that contain practice problems and alternative explanations of the course material. Find anything that you think is helpful or interesting? Let us know and we’ll add it here.

Note that some of these resources will use different notation and terminology than we do in the course. Some examples include:

  • the image of a matrix \(A\), \(\text{im}(A)\), which we call the column space of \(A\), \(\text{colsp}(A)\).
  • the kernel of a matrix \(A\), \(\text{ker}(A)\), which we call the null space of \(A\), \(\text{nullsp}(A)\).
  • the nullity of a matrix \(A\), \(\text{nullity}(A)\), which we call the dimension of the null space of \(A\), \(\dim(\text{nullsp}(A))\).

Textbooks

The one textbook that I’ve referenced the most while developing EECS 245 is Introduction to Linear Algebra by Gilbert Strang. (The textbook itself isn’t free, but this link contains links to several chapters and old exams.) You can find many of his videos online.

The following textbooks are more similar in style to our course notes, in that they’re (somewhat) designed from the perspective of machine learning.

Exams and Practice Problems

Linear Algebra (Chapter 2, Chapter 5)

Linear Regression and Machine Learning (Chapter 1, Chapter 3, Chapter 4)