ORIE 4741

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  • Schedule of Classes - November 13, 2024 8:41AM EST
  • Course Catalog - November 12, 2024 10:26AM EST
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ORIE 4741
Learning with Big Messy Data

Course information provided by the 2023-2024 Catalog.

Modern data sets, whether collected by scientists, engineers, medical researchers, government, financial firms, social networks, or software companies, are often big, messy, and extremely useful. This course addresses scalable robust methods for learning from big messy data. We'll cover techniques for learning with data that is messy - consisting of real numbers, integers, booleans, categoricals, ordinals, graphs, text, sets, and more, with missing entries and with outliers - and that is big - which means we can only use algorithms whose complexity scales linearly in the size of the data. We will cover techniques for cleaning data, supervised and unsupervised learning, finding similar items, model validation, and feature engineering.


Prerequisites/Corequisites Prerequisite: MATH 2940, ENGRD 2700, ENGRD 2110/CS 2110, CS 2800 or equivalents.

When Offered Fall, Spring.

View Enrollment Information

Syllabi: none

  •   Regular Academic Session.  Choose one lecture and one discussion. Combined with: ORIE 5741

  • 4 Credits GradeNoAud

  •  9794 ORIE 4741   LEC 001

  •  

  • Instruction Mode: In Person

  •  9795 ORIE 4741   DIS 201

  •  

  • Instruction Mode: In Person

  •  9796 ORIE 4741   DIS 202

  •  

  • Instruction Mode: In Person

  •  9797 ORIE 4741   DIS 203

  •  

  • Instruction Mode: In Person

  •  9798 ORIE 4741   DIS 204

  •  

  • Instruction Mode: In Person