Linear algebra course core topics

Last week I discussed what principles I am planning to apply to redesign my linear algebra course with best modern practices in mind. The next step is to decide what core topics to cover. But before deciding on the list of core topics, I will use a bottom-up approach and start with student outcomes. First, I want the students to be able to construct and write meaningful mathematical proofs; this means dedicating time to explain the thinking process behind generating proofs. Second, I want the students to see the development of the subject from first principles up to a major milestone. In linear algebra, the major milestone is the classification of finite-dimensional vector spaces. Few mathematical disciplines can boast such a clean and efficient classification of its objects of study.

With these considerations in mind, here is the list of topics I plan to cover:

  • Linear systems: augmented matrix, reduced-row echelon form, Gauss–Jordan elimination, and nonsingular matrices. Prove that there are only 3 types of solution sets.
  • Column vectors: vector operations, linear combinations, linear independence, and spanning sets. Gently introduce the students to the notion of vectors and linear combinations by using explicit objects such as column vectors.
  • Matrices: matrix multiplication, matrix inverses and their relation to linear systems, and column and row spaces. Connect properties of matrices with solutions of linear systems.
  • Vector spaces: abstract vector spaces, subspaces, bases, and dimension. Use column vectors and matrices as simple visual examples of vector spaces. Introduce the rank of a matrix, and connect it with solutions of linear systems.
  • Determinants: properties of determinants and geometric interpretation. Introduce elementary matrices, and use them to prove key properties of determinants.
  • Eigenvalues and eigenvectors: computing eigenvalues and eigenvectors, properties of eigenvalues and eigenvectors, similarity and diagonalization. Show that the similarity relation is an equivalence relation.
  • Linear transformations: kernel and range, injective, surjective, and invertible linear transformations. Prove that a linear transformation of column vectors is multiplication by a fixed matrix.
  • Representations: tie all notions discussed up to this point together. Prove that all vectors spaces (over a fixed field) can be classified by one positive integer, the dimension. Introduce matrix representations of linear transformations, and connect properties of linear transformations with the properties of their matrix representations. Prove that composition of linear transformations corresponds to multiplication of matrices. Discuss change of basis.

What I will not be able to cover are LU-decomposition and inner products. This is a tradeoff I am willing to make to be able to prove the classification of finite-dimensional vector spaces theorem.

In the next post, I will summarize my current thoughts on using software to solve linear algebra problems numerically. I still have not completely made my mind up on how to implement this aspect in my class, and I will welcome any comments and suggestions.

This Post Has 2 Comments

  1. Greg

    If you have a more proof-based course I don’t know if you’ll have a lot of time for teaching students how to write code. At Georgia Tech we recently introduced numerical computations in one of our intro linear algebra courses by having a very small number of assignments that require matlab. It seems to be going well. If your students do not have access to matlab (for free), there is of course octave, for which there are many free online compilers.

    1. Igor

      You are right, Greg. Teaching both proofs and coding in addition to the subject matter is impossible. I don’t plan to teach students how to code. Instead, I’m thinking of providing simple tutorials on YouTube on how to perform specific tasks (e.g., solve a linear system). I talked to my students, and almost all of them have had some prior experience with Python. So, I’ve decided to try Python in Jupyter Notebooks in Google Colab. This is completely free, and students don’t have to install any software locally. Of course, Matlab has a more extensive library of functions related to linear algebra, but my goal is to give the students at least the minimal exposure to using modern software in the context of linear algebra.

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