Algorithms for Data Science

Undergraduate credit

Fundamental techniques and mathematical methods for designing and analyzing algorithms used in data science and machine learning. Topics include randomized algorithms (sketching), streaming algorithms (frequent items), dimensionality reduction (Johnson-Lindenstrauss embeddings), matrix approximation (randomized singular value decomposition), optimization (stochastic gradient descent, Newton's method), and graph algorithms (spectral clustering).