Fall 2025 - 인공지능데이터사이언스학과
Linear Algebra (선형대수)
Instructor: Dr. Muhammad Syafrudin
Course information
This course will cover linear algebra, with an emphasis on its applications. Students will study the theory as well as practical examples/exercises in the Python programming language throughout the lectures. Students are expected to grasp the principles of linear algebra theory and its implementation in the Python programming language by the end of the course.
Lecture
Tue, Thu at 12:00-13:30 KST at 화목12:00-13:30 이201
Office hours
Appointment by email (Office at AI Center 502호)
Prerequisites
Familiar/has experience with basic math and programming experience
Notes
Course material can be downloaded in eCampus and please be aware,
that we will not publicly release the homework assignments this year.
Please note that:
-All lectures are in English.
-Weekly lecture topics may be adjusted or changed without prior notice depending
on the understanding level during the class.
-All lectures will be conducted offline or online depending on the university regulations.
-We will notify you via eCampus/email if there is an update from the university regarding the class.
Please contact me or stop by my office if you have any issues with the course.
Schedule
| 주차(Week) | 강의내용(Class Topic & Contents) | 강의활동유형(Class Type) |
|---|---|---|
| 1 | Course overview, Introduction to python programming | 강의+실습 (Lecture + Practice) |
| 2 | Vectors theory and in Python | 강의+실습 (Lecture + Practice) |
| 3 | Linear functions theory and in Python | 강의+실습 (Lecture + Practice) |
| 4 | Norm and distance theory and in Python | 강의+실습 (Lecture + Practice) |
| 5 | Clustering theory and in Python | 강의+실습 (Lecture + Practice) |
| 6 | Public holiday | Public holiday - 7&9 Oct. |
| 7 | Linear independence theory and in Python | 강의+실습 (Lecture + Practice) |
| 8 | Mid exam | 시험 (Exam) |
| 9 | Matrices theory and in Python | 강의+실습 (Lecture + Practice) |
| 10 | Matrix examples theory and in Python | 강의+실습 (Lecture + Practice) |
| 11 | Linear equations theory and in Python | 강의+실습 (Lecture + Practice) |
| 12 | Linear dynamical systems theory and in Python | 강의+실습 (Lecture + Practice) |
| 13 | Matrix multiplication theory and in Python | 강의+실습 (Lecture + Practice) |
| 14 | Matrix inverses theory and in Python | 강의+실습 (Lecture + Practice) |
| 15 | Least squares theory and in Python | 강의+실습 (Lecture + Practice) |
| 16 | Final exam | 시험 (Exam) |
Grading
The final grade will be calculated using the following weights:
| # | Final Grade Weight |
|---|---|
| Attendance | 10% |
| Assignment(quiz/homework/weekly assignment) | 15% |
| Mid exam | 35% |
| Final exam | 40% |
| Total | 100% |
Assignment
There will be a quiz/homework/weekly assignment to complete. Some of them will be due in a week and some of them in two weeks. You have the option to work and submit the homework in pairs for all the assignments except two which you will do individually.
Submitting an assignment
Instructions for turning in assignments will be posted when the semester starts (in eCampus).
Getting help
For questions about homework, course content, installation, and after you have tried to troubleshoot yourselves, the process to get help is: Post the question in eCampus/group chat and hopefully your peers will answer. Note that in eCampus questions are visible to everyone. For private matters send an email to helpline: udin [at] sju [dot] ac [dot] kr.
Course Policies
Collaboration policy
We encourage you to talk and discuss the assignments with your fellow students (and on eCampus), but you are not allowed to look at any other students assignment or code outside of your pair. Discussion is encouraged, copying is not allowed.
Late day policy
Homework is due before each class. Late submission are not allowed.
Communication to students
Class announcements will be through eCampus. All homework and quizzes will be posted in eCampus. Also all feedback forms. Important note: make sure you have your settings set so you can receive emails from eCampus.
Academic honesty
We give a strong emphasis to Academic Honesty. As a student your best guidelines are to be reasonable and fair. We encourage teamwork for problem sets, but you should not split the homework and you should work on all the problems together.