MAT 167 Applied Linear Algebra Syllabus Page (Fall 2025)
Course Objectives:
To understand the importance of linear algebra and learn its applicability to practical problems, in particular, applications in machine learning/pattern recognition, data mining/search engines, and signal/image processing.
To learn important concepts of linear algebra, such as linear transformations, bases, projections, least squares method, various matrix decompositions such as LU, QR, eigenvalue, and SVD (singular value decomposition).
To enhance your understanding of the above concepts through the use of the Julia programming language.
Lars Eldén: Matrix Methods in Data Mining and Pattern Recognition, SIAM, 2007, ISBN: 978-0-898716-26-9.
Access: The complete first edition (2007) is available for free download via the SIAM Publications Library when connected through the UCD Library VPN.
Errata: Please consult the list of currently known errata.
Note: A second edition was published in 2019; however, this edition is not available for free download through the library. It is, however, a worthwhile investment if you choose to purchase it.
MAT 22A or MAT 67 (i.e., understanding of elementary linear algebra).
Prior experience with a MATLAB-like programming language is required. This course will use the Julia programming language, which is syntactically similar to MATLAB but is open-source and runs on multiple platforms. If you have not previously used Julia, you should install it on your computer and familiarize yourself with its basics through self-study. Additional guidance and resources will be provided in due course.
Julia & Pluto.jl Setup:
Install Julia and Pluto.jl before or during Week 1.
Formal attendance will not be taken; however, I strongly encourage you to attend class regularly. In addition to the textbook material, I often share my own experiences and perspectives on linear algebra that are not written in the text. I may also distribute handouts from time to time.
Whether or not you attend, you are responsible for all material presented in class. While I will make every effort to post announcements via email or on the course website, it is ultimately your responsibility to find out what you missed if you are absent.
Class Web Page:
I will maintain the course’s Canvas site, where all homework assignments and important announcements will be posted. My lecture notes will be posted after each lecture in the Files → Lecture Slides folder. Please check it regularly.
20% Midterm Exam (in class, Wednesday, October 29, 2025)
40% Final Exam (8am-10am, Thursday, December 11, 2025)
Homework:
Homework problems will be assigned after each Friday lecture and posted on the Homework Assignment page.
Due date:3:00 PM on Monday, 10 days after the assignment date (adjusted for university holidays; see the Homework page for details).
Submission: Scan your completed homework and upload it to the Gradescope – Homework section by the due date. Late homework will not be accepted.
Presentation requirements:
Write solutions neatly, accurately, and legibly.
Show all reasoning and computations — final answers alone are not sufficient.
Use complete sentences where appropriate. Work that cannot be followed may be penalized.
Grading:
Only a subset of problems will be graded; graded work will be visible on Gradescope about one week after the due date.
The lowest homework score will be dropped when computing your final grade.
If time permits, some assignments will include problems requiring Julia. These will provide valuable hands-on experience and deepen your understanding of linear algebra.
Workload note: This is a 4 unit course. Expect to spend approximately 9–10 hours per week on homework (about 3 hours outside of class for each lecture hour).
Exams:
There will be one midterm and a final examination:
Midterm: Wednesday, October 29 (in class)
Final Exam: Thursday, December 11, 8:00–10:00 AM (location TBA)
Policies:
All exams are closed book — no textbooks, notes, crib sheets, or outside materials.
Do not bring your own scratch paper or blue books.
Calculators, laptops, cell phones, and other electronic devices are not permitted.
Exams are individual work. Any suspicion of collaboration, copying, or other violations of the UC Davis Student Code of Conduct will be referred to the Student Judicial Board.
The final exam is cumulative, with greater emphasis on topics not covered by the midterm.
Missed Exams:
Midterm: There will be no make‑up midterm. If you miss it due to a catastrophic event (e.g., serious illness, death of an immediate family member), you must provide written proof (e.g., signed letter from a medical doctor). If approved, your course grade will be reweighted (e.g., Homework 45%, Final 55%).
Final Exam: If you miss the final exam due to a catastrophic event and provide written proof, you will receive an Incomplete. You must take a makeup exam in the following quarter to receive a letter grade.