MATH 136
This is my course review for math 136 at University of Washington. I took the course in spring 2015 with Ebru Bekyel. This class is a continuation of math 134 and math 135, which I took the previous two quarters at UW. You can look at the course webpage (archived as of the end of the quarter) for some of the course content.
Content
The first seven weeks or so covered the basics of linear algebra, including finite vector spaces; matrices; linear transformation, kernel and image; eigenvalues and eigenvectors; change of basis; self-adjoint, normal, unitary, and orthogonal matrices; and diagonalization. (These topics were covered in the first and second midterms.) Following this, the course went on to cover some vector calculus (a continuation from what we covered in math 135).
Textbooks
We used Treil’s Linear Algebra Done Wrong for linear algebra, and Calculus by Salas, Hille, and Etgen (the same book we used in math 134 and 135) for vector calculus.
Difficulty
I thought the linear algebra part of the course was much more difficult than any part of the previous two quarters. In high school, we did some calculations involving matrices, but this was at a very basic level, so I didn’t have too strong of a background in linear algebra1. Moreover the abstract nature of linear algebra (at least, compared to calculus) made it difficult to “grok” many of the theorems that we covered in class.
Camaraderie
I only went to two TA office hours this quarter (for the two midterm review sessions) and attended one group study session (right before the second midterm). Overall, I thought that while people in the class had gotten more used to each other, that there was perhaps less group work when working on assignments.
After the last final the class got together to have lunch at the HUB.
This is unlike in math 134 when I had already partially self-studied proof-based calculus, making the course relatively easier, and also unlike the vector calculus part of 136, where everything was very visual so it was easy to have good intution when working on problems.↩︎