Spring 2025 Marketing B9653 section 001

MS Machine Learning

Call Number 16812
Day & Time
Location
T 6:00pm-9:00pm
420 Kravis Hall
Points 1.5
Grading Mode Standard
Approvals Required None
Instructor George Lentzas
Type LECTURE
Method of Instruction In-Person
Course Description

This course is the first of two courses that will introduce students to the exciting and growing literature in machine learning / AI with a focus on applications in finance and marketing. We will cover topics such as regularization, tree methods, bagging/boosting, support vector machines and recommendation algorithms. In the process, we will review several real-world applications drawn from the areas of finance and marketing. Students are expected to be familiar with basic probability theory, linear algebra, and multiple linear regression. Some familiarity with (and willingness to learn) programming is a prerequisite as we will make extensive use of the programming language R.

Web Site Vergil
Department MARKETING
Enrollment 53 students (74 max) as of 10:06AM Saturday, February 22, 2025
Subject Marketing
Number B9653
Section 001
Division School of Business
Open To Business, Engineering:Graduate, Journalism
Section key 20251MRKT9653B001