- Schedule of Classes - June 18, 2017 7:14PM EDT
- Course Catalog - June 14, 2017 7:15PM EDT
Course information provided by the Courses of Study 2016-2017.
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.
When Offered Spring.
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.
Distribution Category (MQR-AS)
Regular Academic Session.
Credits and Grading Basis
4 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- TRKennedy Hall 116-Call Aud
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