Content

Students will learn algorithms and techniques to address large scale graph analytics, including: graph analytics theory (centrality measures, connected components, graph clustering); graph properties for random, small-world, and scale free graphs; graph metrics for robustness and resiliency; graph algorithms for reference problems.

Students will be involved in project activities.

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Instructor(s)

Marina Ribaudo

Marina Ribaudo

Associate Professor of Computer Science

Web Applications

Lorenzo Rosasco

Lorenzo Rosasco

Associate Professor of Computer Science

Machine Learning and Learning Algorithms

Effort Breakdown

  • Class
  • Lab
  • Project
  • Outside Preparation