Course: Math 530 Linear Algebra for Statisticians
Meeting times: MWF 2:20 pm-3:20 pm
Office hours: MWF 3:30 - 4:30 (Zoom ID 949 5616 9870)
Prerequisites: Math 304 (Linear Algebra) or equivalent.
Text: The book that I will use 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. Learning outcomes: I plan to cover Projectors, various matrix decompositions including SVD, QR and Cholesky, their application to linear regression, and Multivariate Gaussian distribution.
Computing: I will ask students to subscribe to Datacamp.com for 1 or 2 months 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.
Communication: I will mostly use Piazza Forum (https://piazza.com). 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.
Exams: The will be one midterm and one “final” exam. The final will be in November before Thanksgiving break.
The lectures will continue after Thanksgiving with an online test given in the period December 8 – December 10.
Grading: Linear Algebra Homework 25% Python Courses + Python Homework 25% Midterm 20% Final Exam 25% Online test in December: 5%