ORIE 5741

ORIE 5741

Course information provided by the Courses of Study 2024-2025.

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.

When Offered Fall, Spring.

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

View Enrollment Information

Syllabi:
  •   Regular Academic Session.  Choose one lecture and one discussion. Combined with: ORIE 3741

  • 4 Credits GradeNoAud

  •  7650 ORIE 5741   LEC 001

    • TR
    • Jan 21 - May 6, 2025
    • Shafiee, S

  • Instruction Mode: In Person
    Enrollment limited to: Operations Research and Information Engineering (ORIE) Master of Engineering (M.Eng.) students during pre-enroll, others may enroll during add/drop.

  •  7651 ORIE 5741   DIS 201

    • M
    • Jan 21 - May 6, 2025
    • Shafiee, S

  • Instruction Mode: In Person

  •  7652 ORIE 5741   DIS 202

    • T
    • Jan 21 - May 6, 2025
    • Shafiee, S

  • Instruction Mode: In Person

  •  7653 ORIE 5741   DIS 203

    • W
    • Jan 21 - May 6, 2025
    • Shafiee, S

  • Instruction Mode: In Person

  •  7654 ORIE 5741   DIS 204

    • W
    • Jan 21 - May 6, 2025
    • Shafiee, S

  • Instruction Mode: In Person