ORIE 4741

ORIE 4741

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

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. The course will culminate in a final project in which students extract useful information from a big messy data set.

When Offered Fall.

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

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Choose one lecture. Discussion optional. Combined with: ORIE 5741

  • 3-4 Credits GradeNoAud

  • 11293 ORIE 4741   LEC 001

  • Instruction Mode: In Person
    **For a 3 credit course register for the Lecture. **For a 4 credit course register for the lecture and a discussion. MEng Students must enroll in ORIE 5741.

  • 11294 ORIE 4741   DIS 201

  • Instruction Mode: In Person
    Add a discussion for 4credits

  • 11295 ORIE 4741   DIS 202

    • W Upson Hall 222
    • Aug 26 - Dec 7, 2021
    • Udell, M

  • Instruction Mode: In Person
    Add a discussion for 4credits

  • 11296 ORIE 4741   DIS 203

    • R Upson Hall 206
    • Aug 26 - Dec 7, 2021
    • Udell, M

  • Instruction Mode: In Person
    Add a discussion for 4credits

  • 11297 ORIE 4741   DIS 204

  • Instruction Mode: In Person
    Add a discussion for 4credits