Instructor: Chi-Kwong Li
Course description:
The goal of the course is to equip students with basic techniques in linear algebra. Students are expected to read the textbook, especially, the sections concerning applications. We can arrange small group discussions for students interested in specific applications outside classes. The diescussions may lead to undergraduate research projects.
The course will cover Chapters 1 -- 6 of the textbook, and some additional topics: singular value decomposition (SVD), and data compression. If time permits, I will also discuss tensor products and their applications in data science, AI, and quantum computing.
Homework
Homework Exam. 1. Exam. 2. Final
(weekly) (Oct. 5) (Nov. 16) (Dec. 19, 9:00am.- noon)
25% 20% 20% 35%
Grades (for homework, quizzes, exams, final grade, etc.):
%: 0 - 60 - 65 - 70 - 75 - 80 - 83 - 87 - 90 - 93 - 100
F D C- C C+ B- B B+ A- A
(Extra credits may add another 5-6%)
Classnotes See the information page in the blackboard site.
Homework List See the assignment page in the blackboard site.