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.
UPDATE #5: As mentioned above, I’m teaching a complementary installment of this class this semester. I fixed several typos throughout, and I added a new section on embeddings from pairwise data.
UPDATE #6: Added a section on the clique problem.
UPDATE #7: Added a section on the Lovasz number.
UPDATE #8: Added a section on planted clique.
UPDATE #9: Added sections on maximum cut and minimum normalized cut.
UPDATE #10: Added a section on k-means clustering.
UPDATE #11: Started a chapter on compressed sensing.
UPDATE #12: Started a section on uniform guarantees.