INFO 2950

INFO 2950

Course information provided by the 2014-2015 Catalog.

Teaches basic mathematical methods for information science, with applications to data science. Topics include discrete probability, Bayesian methods, graph theory, power law distributions, Markov models, and hidden Markov models. Uses examples and applications from various areas of information science such as the structure of the web, genomics, social networks, natural language processing, and signal processing.  Some assignments require python programming.


Prerequisites/Corequisites Prerequisite: An introductory statistics course from the approved list of accepted statistics courses found at http://infosci.cornell.edu/academics/degrees/ba-college-arts-sciences/degree-requirements/core-requirements and an introductory programming class, or permission of instructor. Corequisite: MATH 2310 or equivalent.

Distribution Category (MQR)

When Offered Fall.

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Syllabi: none
  •   Regular Academic Session. 

  • 4 Credits Stdnt Opt

  • 13120 INFO 2950   LEC 001

  • Instruction Mode: