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.
Division: Marketing
Spring 2025
B9653 - 001
Part of Term
MS - A Term
Section Syllabus
Download Syllabus
Section Notes
Attendance at the first class is mandatory for all enrolled students and those on a waitlist or who hope to add the class during Add/Drop.
Day(s)
Date(s)
Start/End Time
Room
-
Tuesday 01/27/2025 - 03/07/2025 6:00PM - 9:00PM Kravis 420
Spring 2024
B9653 - 001
Day(s)
Date(s)
Start/End Time
Room
-
Tuesday 01/22/2024 - 03/01/2024 6:00PM - 9:00PM Kravis 670