Math 2130 (Fall 2025) :
Linear Algebra
About this course
Welcome to Math 2130 : Linear Algebra! This is a linear algebra class focussed on matrix methods, calculations, and applications. This class covers essentially the same material as Linear Algebra for Mathematics Majors (Math 2135), but has less emphasis on mathematical proofs.
Contact information
- Instructor : Jonathan Wise
- Office : Math 204
- E-mail : jonathan.wise@colorado.edu
You may also contact me anonymously.
Office hours
My office is Room 204 in the Math Department. My office hours sometimes change, so I maintain a calendar showing the times I will be available. If I need to hold an office hour over Zoom, it will be indicated on the calendar. You will be able to find the Zoom link on Canvas. I am often in my office outside of those hours, and I'll be happy to answer questions if you drop by outside of office hours, provided I am not busy with something else. I am also happy to make an appointment if my office hours are not convenient for you.
Syllabus
The following are the main topics of this course. Additional topics may be covered if time permits.
The textbook for this course is a modified version of David Austin. Understanding Linear Algebra. This book was published David Austin under a Creative Commons license. That permits me to make changes to the text to adapt it to our class. I encourage you to make use of other resources. For example : Gilbert Strang has plentiful supplementary materials available online through MIT's OpenCourseWare, both for you and for me to rely upon. 3blue1brown has a lecture series called Essence of linear algebra. You may also enjoy some of 3blue1brown's other mathematical videos. Khan Academy has a course on linear algebra.
Large language models like Chat-GPT, Gemini, or Claude can also be an excellent resource when used responsibly (and skillfully). I encourage you to make use of these on your homework, especially as a source of feedback. Officially Math 2300 (Calculus 2) is required as a prerequisite, but calculus will only be used in a few special applications, if at all. For the most part, the only background that is strictly required is high school algebra. We will learn quite a few definitions, theorems, and algorithms in this class. The following list is meant to give an idea of the different levels of mastery you can achieve with them. The letters should also give a rough idea of how those levels of mastery will correspond to a final grade. For example, one of the most important things we will learn in this class is the Gaußian elimination algorithm for putting a matrix in row echelon or reduced row echelon form. Here is an idea of how I might interpret the above criteria for the Gaußian elimination algorithm: You are required to spend at least one hour working on linear algebra between every two classes. I will give assignments, but you may allocate this time as you wish : by reading the textbook, doing homework exercises, doing unassigned problems, reviewing notes, watching lectures online, conversing with a large language model (about linear algebra), etc. I will ask you to submit some record of how you spent your hour on Canvas. This might be a scan of completed homework problems, but it could also be a list of the sections you studied, or links to videos you watched, or a transcript of your discussion with Chat-GPT. Along with your submission, you should enter a comment with the number of minutes you spent on your homework. Your final grade will be determined from 5 components:
Textbook
Prerequisites
Course goals
Homework
Grading
| Component | Contribution | Dates |
|---|---|---|
| Daily Work | 10% | Daily |
| Midterm Exam | 20% | 15 October at 6pm (MUEN E0046) |
| Quizzes | 25% | Weekly |
| Poster | 10% | 1 Dec — 5 Dec |
| Final Exam | 35% | Schedule |
The Daily Work component comprises daily homework assignments and some in-class group work.
I grade on a modified GPA scale. This means that you will be scored between 0 and 5 on each assignment, and the average of this numerical (weighted by the above proportions) translates directly to your grade in the course according to the following table :
| Number | Letter |
|---|---|
| 4.5 | A |
| 3.5 | B |
| 2.5 | C |
| 1.5 | D |
| 0.5 | F |
Intermediate scores (A-, C+, etc.) are possible even though they are not shown in the table.
You should be aware of several things about this grading scheme. First, it is much more forgiving of missing assignments than the traditional 10-point grading scale where a score below 50% registers as an F. For this reason, we will not need a special policy for missed daily assignments. Second, your scores may be lower, percentage-wise, than you are accustomed to : solving half of the problems on an exam correctly might translate to a C- rather than to an F.
The scoring on the assessments will be weighted numerical average of scores on individual problems, curved to reflect the learning goals.
Exam structure
The exams may contain group work components and be administered over more than one day. Further details will appear here (and be announced in class) later in the semester.
Missed exams will be handled on a case-by-case basis.
Posters
At the end of the semester, you will present posters about advanced topics and/or applications of linear algebra. You will work in groups of 2. A small number of groups of 3 will also be allowed.
There will several intermediate stages :
| Component | Due date |
|---|---|
| Choose groups | 8 October |
| Choose topics | 22 October |
| Submit drafts | 7 November |
| Final posters | 21 November |
Overleaf has a gallery of poster templates that you can use to help get started.
Academic honesty
I encourage you to consult outside sources, use the internet, and collaborate with your peers. However, there are important rules to ensure that you use these opportunities in an academically honest way.
- Anything with your name on it must be your work, and only your work, and it should accurately reflect your understanding.
- Expect plagiarism to be treated severely. Deliberate plagiarism will be reported to the Honor Code Office.
To avoid plagiarism, you should always keep track of the references you use and cite all resources you consult, whether they are textbooks, tutors, websites, classmates, large language models, or any other form of assistance. Using others' words verbatim, without attribution, is absolutely forbidden, but so is using others' words with small modifications. The ideal way to use a source is to study it, understand it, put it away, use your own words to express your newfound understanding, and then cite the source as an inspiration for your work.
The particular format for your citations is not important, provided that they indicate what the source was and they provide sufficient information for a reader to find the source. Textual references should therefore be specific (include a page number, for example). Internet references should have a working URL.
Special accommodations, classroom behavior, and the honor code
The Office of Academic Affairs officially recommends a number of statements for course syllabi, all of which are fully supported in this class.
If you need special acommodation of any kind in this class, or are uncomfortable in the class for any reason, please contact me and I will do my best to remedy the situation. You may contact me in person or send me a comment anonymously.
Questions
Do you have a question or comment about the course? The answer might be in the course policies, on this page. If your question isn't answered in the course policies, please send me an email. Or, if you prefer, you may send me a comment anonymously.