Distributed Learning and Optimization ((3))

Catalog description

Distributed computing architectures have led to adaptation of sequential algorithms to a distributed computation domain. Computer science subfields such as machine learning and optimization benefit greatly from these distributed architectures, thus have been adapted. Topics for this class include distributed learning and optimization, graph analysis, scaling, complexity analysis and evaluation of current platforms. Each week lecture meets for 150 minutes.