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Course Information

Repository Guide | English | 한국어

Last Updated: 2026-10-27

Table of Contents


1. Instructor and Research Lab

  • Instructor: Prof. Shinhoo Kang

2. Schedule and Class Format

  • Language of Instruction: English
  • Intended For: Third-year students
  • Class Format: Each week consists of 2 hours of online classes and 1 hour of in-person class.
  • Term Structure: The semester runs for 16 weeks, with the midterm exam in week 8 and the final exam in week 16.
  • Course Description: Students learn vector spaces, linear transformations, solving systems of linear equations, and eigenvalues and eigenvectors.

3. Assessment

Item Weight
Attendance 6%
Assignments 24%
Midterm Exam 30%
Final Exam 40%
  • The midterm exam covers the topics taught up to the exam date, and the final exam focuses mainly on the material learned after the midterm.
  • The assignments are weekly homework assignments, each with one week to complete and submitted online. The syllabus announced twelve homework assignments, and the first lecture deck announced ten.
  • Grades are disclosed.

4. Course Policies

  • Students who have a conflict with an exam must notify the instructor immediately.
  • The submission deadline is strict, and all late assignments are graded zero. No exceptions to the homework policy are made without prior instructor approval, and medical exceptions require proper documentation.
  • Students are encouraged to discuss class topics among themselves, but each assignment must be completed independently. Any sharing of code or plagiarism is considered academic dishonesty and results in strict disciplinary action.
  • Regular attendance is expected. Except for unavoidable absences deemed necessary by the instructor, no grade is assigned to students absent for one third or more of the class days.
  • This course encourages the use of AI agents.

5. Weekly Plan and Progress

Week Planned Topic Lecture Decks Covered Chapter Topic Covered
W01 Introduction to Vectors Introduction, N1 Ch. 1 Introduction, Vectors and Matrices
W02 Solving Linear Equations - Ch. 1 Vectors and Matrices (continued)
W03 Solving Linear Equations N2 Ch. 2 Solving Linear Equations
W04 Four Fundamental Subspaces - Ch. 2 Solving Linear Equations (continued)
W05 Four Fundamental Subspaces N3 (A) Ch. 3 Vector Spaces and Subspaces
W06 Orthogonality N3 (B) Ch. 3 Vector Spaces and Subspaces (continued)
W07 Orthogonality - - Review
W08 Midterm exam Midterm - -
W09 Determinants N4-1, N4-2 Ch. 4 Orthogonality, Projections
W10 Eigenvalues and eigenvectors N4-3, N4-4, N4-5 Ch. 4 Least Squares, Orthogonal Basis, Pseudoinverse
W11 Eigenvalues and eigenvectors N5-1, N5-2, N5-3 Ch. 5 Determinants
W12 Singular Value Decomposition N6-1, N6-2 Ch. 6 Eigenvalues, Diagonalization
W13 Linear Transformation N6-3, N6-5 Ch. 6 Symmetric Positive Definite Matrices, Linear Differential Equations
W14 Linear Transformation N7-1, N7-2, N7-3 Ch. 7 SVD, Image Processing, PCA
W15 Linear Algebra in Optimization N8-1, N8-2, N8-3 Ch. 8 Linear Transformations
W16 Final Exam Final - -
  • The planned topics follow the weekly course plan of the syllabus. Linear Algebra in Optimization (Chapter 9), planned for week 15, is not among the decks covered.
  • The course plan in the Introduction deck sets the textbook range and key concept of each week as Chapter 1 (vectors) in week 1, Chapter 2 ($Ax = b$) in week 2, Chapter 3 ($Ax = b$) in week 3, Sections 3.1-3.2 (column space) in week 4, Sections 3.3-3.5 (nullspace) in week 5, Sections 4.1-4.3 (orthogonality) in week 6, Sections 4.4-4.5 (orthogonality) in week 7, Chapter 5 (determinants) in week 9, Sections 6.1-6.3 (eigendecomposition) in week 10, Sections 6.4-6.5 (eigendecomposition) in week 11, Chapter 7 (SVD) in week 12, Chapter 8 (linear maps) in weeks 13 and 14, and Chapter 9 (SGD) in week 15.
  • The deck numbers N1-N8 match the textbook chapters, and the second number in names such as N4-1 is the section number within that chapter.
  • The decks of each chapter are organized in the lecture note of the Ch## unit with the same number, and the units are listed in the Lecture-Notes index.

6. References

Type Contents
Textbook G. Strang, Introduction to Linear Algebra, 6th ed. Wellesley, MA, USA: Wellesley-Cambridge Press, 2023.
Reference D. C. Lay, S. R. Lay, and J. J. McDonald, Linear Algebra and Its Applications, 5th ed. Boston, MA, USA: Pearson, 2016.
Reference G. Strang, Linear Algebra and Its Applications, 4th ed. Belmont, CA, USA: Thomson Brooks/Cole, 2006.
Reference S. Lang, Introduction to Linear Algebra, 2nd ed. New York, NY, USA: Springer-Verlag, 1986.
Lecture Slides Chapter slides and section-by-section online lecture videos provided by the instructor