This spring, I’m teaching a graduate-level special topics course called “Mathematics of Data Science” at the Ohio State University. This will be a research-oriented class, and in lecture, I plan to cover some of the important ideas from convex optimization, probability, dimensionality reduction, clustering, and sparsity.
The current draft consists of a chapter on convex optimization. I will update the above link periodically. Feel free to comment below.
UPDATE #1: Lightly edited Chapter 1 and added a chapter on probability.
UPDATE #2: Lightly edited Chapter 2 and added a section on PCA.
UPDATE #3: Added a section on random projection.
UPDATE #4: Lightly edited Chapter 3. The semester is over, so I don’t plan to update these notes again until I teach a complementary special topics course next year.