3 Credits
A course in mathematical data science with an emphasis on theory. The course will also highlight important applications and students will have the opportunity to program some standard algorithms. The topics to be covered include principal component analysis, algorithms in numerical linear algebra, unsupervised clustering and density methods, nearest neighbor classifiers, supervised methods such as support vector machines and neural networks, and spectral graph theory, with applications in areas like image processing and network analysis.