Course description

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

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.

No upcoming classes were found.

Previously offered classes

Summer 2023: Ithaca campus

Section ID:INFO 2950 001-LEC
Number:1270
Session:Summer 3-week 1
Class dates:May 30-June 16, 2023
Time / room:M-F 10 AM - 11:20 AM / Upson Hall 146
M-F 1 PM - 2:20 PM / Upson Hall 146
Mode of instruction:In person
Credit:4
Instructor:Soltoff, B. (bcs88)
Related:INFO 2950 201-DIS

Summer 2023: Ithaca campus

Section ID:INFO 2950 201-DIS
Number:1271
Session:Summer 3-week 1
Class dates:May 30-June 16, 2023
Final exam/project due:TBA (see Final exams)
Time / room:M-F 2:35 PM - 3:30 PM / Upson Hall 146
Mode of instruction:In person
Credit:0
Grade:Graded
Instructor:Soltoff, B. (bcs88)
Max. enroll:15
Related:You will be auto-enrolled in INFO 2950 001-LEC
To enroll:Enrollment for this class is closed.