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Math 530: Linear Algebra for Statisticians

Basic Info

  • Meeting times/Place: MWF 10:50am -11:50am at S2 255
  • Office hours: MWF 9:45 - 10:30 (in person, office WH136), Tue 4:00PM - 5:00PM (via Zoom, ID 949 5616 9870), or by appointment
  • Prerequisites: Math 304 (Linear Algebra), 329, 330 or equivalent.


  1. The main book that will be used is “Numerical Linear Algebra” by Lloyd Trefethen, David Bau. We will skip some aspects which are important in Numerical Analysis but are not very relevant for Statistics, such as Conditioning and Stability.
  2. The second book is Strang “Linear Algebra with Applications”. We will use some topics and exercises from this book.

The electronic versions of these books is available at the course Piazza webpage.

Online resources

There is a course based on the Strang's book available at Linear Algebra at MIT OCW. It might be helpful for some topics.

Learning outcomes

I plan to cover Projectors, various matrix decompositions including SVD, QR and Cholesky, their application to linear regression, and Multivariate Gaussian distribution.


I will ask students to subscribe to for 1 or 2 months (do not pay annual subscription!) and take 3 Datacamp courses in “Python”. Specifically, “Introduction to Python”, “Intermediate Python”, “Python Data Science Toolbox 1”. Every datacamp course will be expected to be finished in 1 or 2 weeks and at the end of each one, the student will send me a proof that he or she has passed the course. There will be additional Python based exercises.


I will mostly use Piazza Forum ( In particular, I will post all announcements and lecture notes on this website. So make sure that you are enrolled at Piazza. Since this is a forum, questions and answers by students are encouraged. I will use MyCourses/Blackboard only minimally.

Homework Policy

The homework on linear algebra will be assigned using Gradescope. (Use code JBVEW5 for Gradescope to add yourself to the course if you are not yet enrolled.)

There will be a deduction of 25% of the grade for homework assignments that are not typeset using LaTeX. (For users with no experience with LaTex, I suggest trying “”.)

There will be a deduction of 15% of the grade for each day homeworks are late (the final grade for a late homework that is N days late will be 0.85^N times the real grade). Homeworks may be discussed with classmates but must be written and submitted individually.

There will also be some Python homework which will have to be submitted both on Gradescope (pdf file) and through a Google form (source file).


The will be one midterm and one final exam.


 The grading scale will be different for undergraduate and graduate students. 
  • Linear Algebra Homework 25%
  • Python Courses + Python Homework 25%
  • Midterm 20%
  • Final Exam 30%

Tentative Schedule

Midterm Exam October 13
people/kargin/math530_fall2020.txt · Last modified: 2021/08/30 15:10 by kargin