Course information

A more detailed description of this information can be found in the course syllabus.

Lectures

Monday & Wednesday & Friday 9:55am–10:45am CT in B302 Birge Hall

Textbook

Numerical Linear Algebra, by Lloyd N. Trefethen and David Bau (required)
Applied Numerical Linear Algebra, by James W. Demmel (optional)
Data-Driven Science and Engineering, by Steven L. Brunton and J. Nathan Kutz (optional)

Suggested watchlist

Linear algebra refresher: 3Blue1Brown: Essence of linear algebra
AI and education: Veritasium: What everyone gets wrong about AI and learning

Grades

The final letter grade will be based on homework assignments (20%), in-class quizzes (15%), one mid-term exam (25%), and the final exam (40%).

Homework

There will be seven homework assignments. The first is due on Tuesday February 3rd, and the remainder are due at 2 week intervals. Homework assignments will be due at 5pm CT on the course Canvas site. When computing the final homework grade for the course, the single lowest score will be dropped, and the average taken over the remaining five. Please refer to the late-day policy about homework extensions.

In-class quizzes

This page contains logistical details about the in-class quizzes. Starting in Week 2, there will be a short in-class quiz every Wednesday, except during the week of the midterm exam. Each quiz will assess material from recent lectures and may also include questions from the previous quiz. In the following week, students can retake the previous week's quiz and the higher grade will be kept. When calculating the final grade, the lowest two quiz scores will be dropped. There will be no make-up quizzes.

Exams

There will be one mid-term exam and one final exam. The mid-term exam is scheduled for Wednesday March 11th during regular class time. The final exam is scheduled for Monday May 4th from 2:45–4:45pm. The final exam location will be announced later. One page of letter-sized handwritten notes (both sides) is allowed for each exam, and should be turned in with the exam. Students also have the option to overwrite their mid-term exam score with their final exam score if the final is higher—see this page for more details on exams.

Honors or graduate credit

If you are taking the course for honors or graduate credit, you will be required to submit a 3–5 page paper on a topic in numerical linear algebra that is not covered in Math/CS 513 or a topic you hope to explore further. This page contains logistical details about the honors/graduate paper. The honors/graduate paper will be 5% of your final grade, and the homework assignments percentage will be reduced to 15%.

Academic integrity policy

Please refer to the course syllabus for details on the academic integrity policy. Discussion and the exchange of ideas are essential to doing academic work. For assignments in this course, you are encouraged to consult with your classmates as you work on homework assignments. However, after discussions with peers, make sure that you can work through the problem yourself and ensure that any answers you submit for evaluation are the result of your own efforts. You must list the names of students with whom you have collaborated on homework assignments.
In addition, you must cite any books, articles, websites, lectures, etc. that have helped you with your work using appropriate citation practices. Using homework solutions from previous years is forbidden. Failure to disclose collaboration on homework assignments, or failure to cite resources, will be treated as academic misconduct; it will be evaluated on a case-by-case basis.
The teaching team reserves the right to use the Gradescope Review Similarity tool to check student code submissions for similarity. Cases of excessive similarity will be reviewed manually by the teaching team and addressed on a case-by-case basis.

AI policy

While generative artificial intelligence (AI) is powerful and rapidly evolving, it is also prone to producing incorrect code, fabricated references, and misleading summaries. To become critical and responsible users of modern tools, students are expected to learn independently—understanding not just what works, but why it works—so that you can adapt and apply these tools effectively and accurately. Responsible use of such tools requires, first and foremost, a strong foundation in the subject matter.
The use of generative AI tools and applications (including, but not limited to ChatGPT, Copilot, Claude, etc.) in this course is governed by the following policies:

• Prohibited uses: All submitted assignments for this course must be a student's own work. The use of generative AI to draft or produce writeups, solutions, proofs, or code is prohibited. Such use constitutes a violation of UW–Madison’s academic misconduct policy, specifically UWS 14.03(1)b.
• Permitted uses: Limited and strategic use of AI is allowed in the following cases:
  • Copyediting: AI may be used to check grammar or to assist with formatting.
  • Plotting: AI may be used to assist with plotting commands that are not demonstrated in class. However, students must verify and be able to explain any submitted plotting code.
  • Literature review: AI may be used as a "search engine" to help identify potential references. You must independently verify all findings with cited traditional and verifiable sources. AI-generated summaries cannot substitute for reading the original papers, as they are often skewed by the prompt and may provide incorrect information.
• Disclosure requirement: If AI is used in any of the permitted ways, students must include a paragraph in the writeup stating how AI was used, and clearly note the usage in comments within your code. Students should be prepared to provide the exact prompt(s) used if requested by the teaching staff. Failure to disclose AI use will be treated as academic misconduct.