Sep 29, 2022  
2018-2019 Undergraduate Catalog 
    
2018-2019 Undergraduate Catalog [ARCHIVED CATALOG]

Add to Portfolio (opens a new window)

STAT 761 - Discrete Optimization and Scalability for Data Science

Credits: 3

Topics covered include computational complexity, NP-hardness, data as networks, graph theoretic algorithms, exact, approximation, heuristic and online algorithms, and connections between convex and non-convex optimization problems.  The theory and algorithms are applied to data science problems arising in statistical machine learning, statistical clustering, design of experiments, observational studies, sampling, and variable selection.  Applications may be motivated using data from social networks, search engines, the stock market and elections.

Requisites
Prerequisites: (STAT 705 or STAT 713) and STAT 720 and programming knowledge (e.g. STAT 726).

Typically Offered
Spring-Odd Years


View the Fall 2022 Course Schedule

View the Spring 2022 Course Schedule

View the Summer 2021 Course Schedule




Add to Portfolio (opens a new window)