Algorithms for Data Science
EECS 498.004 - Special Topics
Instructor: Euiwoong Lee
This course will introduce algorithmic and theoretical aspects of data science.
With the emergence of machine learning and data science, providing theoretical
foundations for them will become increasingly important. The course will cover
several important algorithms in data science and see how their performances can
be analyzed. While fundamental ideas covered in EECS 376 (e.g., design and
analysis of algorithms, NP-hardness, etc.) will be still important, some topics will
introduce new concepts and ideas, including sublinear time algorithms,
algorithms for continuous domains, and average-case analysis.
A tentative list of topics: Randomized methods for big data (streaming
algorithms, dimensionality reduction), spectral methods (spectral graph theory
and spectral clustering), continuous optimization (convex optimization, its
applications to discrete optimization, and gradient descent), average-case
problems (compressed sensing, stochastic block model).
Prerequisites: EECS 376 and linear algebra.
Course description: Design and analysis of algorithms for problems arising in data
science, with an emphasis on differences and connections between the traditional
discrete algorithms. Topics include randomized methods for big data (hashing,
streaming, and dimension reduction), spectral methods (singular value
decomposition, spectral clustering/partitioning), continuous optimization (convex
analysis, gradient descent), and average-case problems (compressed sensing,
stochastic block model).
