EGRMGMT 588: Machine Learning Principles and Applications

Please be advised: the information contained on this page is a general overview of the course. As course information is subject to change from one semester to another, please check DukeHub for the most accurate and up-to-date information about EGRMGMT courses.

At a Glance

  • Instructor(s): (Louis) Daniel Egger
  • Semester(s) typically taught: Spring
  • Last taught: Spring 2022
  • Units: 3.0
  • Grading scale: Graded (A-F)
  • Required or elective for MEM degree? Elective
  • If elective, applicable elective track(s): Data Analytics and Machine Learning, Software Management
  • Pre-requisites: n/a
  • Recommended previous courses: An introductory data science course

Course Description/Synopsis (from DukeHub) 

This course focuses on understanding how machine learning (ML) works and case studies of its successful application to a wide range of problem types, from better forecasting customer behavior, to playing Go, to responding appropriately to human speech. Students will learn the basic mathematical principles behind establishing reliable ML performance, and have an opportunity to experiment with various ML algorithms and observe how they perform on real world data. The course does not require any prior programming experience.

Course Syllabus (Previous)

EGRMGMT 588.01 Syllabus, Spring 2020

A Word From the Faculty/TA

Please check back for this at a later time.

Previous Course Evaluations

  • None available at this time (was not taught in Spring 2021 or Fall 2021)

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