INFO 2950

INFO 2950

Course information provided by the Courses of Study 2021-2022.

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. Assignments require python programming.  

When Offered Fall.

Prerequisites/Corequisites Prerequisite: MATH 1710 or equivalent, CS 1110 or CS 1112, or permission of instructor.

Distribution Category (MQR-AS, SDS-AS)

Comments Information Science majors must complete this class prior to their senior year.

View Enrollment Information

Syllabi:
  •   Regular Academic Session.  Choose one lecture and one discussion.

  • 4 Credits Graded

  • 17592 INFO 2950   LEC 001

  • Instruction Mode: In Person

  • 17593 INFO 2950   DIS 201

  • Instruction Mode: In Person

  • 17594 INFO 2950   DIS 202

  • Instruction Mode: In Person

  • 17595 INFO 2950   DIS 203

  • Instruction Mode: In Person

  • 17596 INFO 2950   DIS 204

    • F Statler Hall 445
    • Aug 26 - Dec 7, 2021
    • Wilkens, M

  • Instruction Mode: In Person

  • 17597 INFO 2950   DIS 205

    • F Statler Hall 165
    • Aug 26 - Dec 7, 2021
    • Wilkens, M

  • Instruction Mode: In Person

  • 17598 INFO 2950   DIS 206

    • F Statler Hall 341
    • Aug 26 - Dec 7, 2021
    • Wilkens, M

  • Instruction Mode: In Person

  • 17599 INFO 2950   DIS 207

    • F Statler Hall 198
    • Aug 26 - Dec 7, 2021
    • Wilkens, M

  • Instruction Mode: In Person

  • 17600 INFO 2950   DIS 208

    • F Statler Hall 165
    • Aug 26 - Dec 7, 2021
    • Wilkens, M

  • Instruction Mode: In Person

  • 17601 INFO 2950   DIS 209

  • Instruction Mode: In Person