Math 309: Intermediate Linear Algebra, Spring 2020

Instructor: Chi-Kwong Li

Course description: We will study linear algebra techniques in applied topics including such as large matrix computation, image processing, compressed sensing, optimization, neural nets, stochastic gradient descent and backpropagation.

Textbook: Gilbert Strang, Linear Algebra and Learning from Data, Wellesley Cambridge Press.

Some useful references

Homework will be assigned every lecture and due the following Friday (noon).
Homework help sessions will be conducted on Thursday, 11:00-noon Jones 113 or 131. Of course, help is also available at other office hours and by appointment.

Challenging problems will be assigned from time to time;
extra-credits will be given to successful (or partially successful) attempts.
You have to use LaTex to typset mathematical document.


     Exams:  Mid-term 1  Feb.  27  1:20 hrs  (9:30-11:50)
             Mid-term 2  April 2   1:20 hrs  (9:30-11:50)
             Final       May  13    3    hrs  (9:00-noon)
     Grades (for homework, 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
     Assessment: Homework   Mid-term I   Mid-term II    Final  
                   25%        20%           20%          35%
               (Extra credit problems may add up to another 5%)

     One may choose to do a project for 15% (subject to approval) 
     so that the Final will weight 20% for the final assessment.

Exam 1 and sample soluton.

Exam 2 with sample solution. [ Tex file.]

Final examination paper. [Tex file.]

Class notes (Under continuous revision)

Homework list