Machine Learning I (463-0-21)

Instructors

Edward Carl Malthouse

Meeting Info

Fisk Hall 115: Tues, Thurs 2:00PM - 3:50PM

Overview of class

Machine Learning I covers building, interpreting and applying predictive models used in marketing communications research. Students will explore many of the issues that arise in building such models, e.g., exploratory vs. confirmatory studies, inductive vs. deductive reasoning, multi-collinearity, heteroscedasticity, nonlinearity, interactions, model selection, regularization, bias-variance tradeoff, extrapolation, and the curse of dimensionality. The course will help students understand how and why different methods work, which methods are well-suited for certain situations, and the extent of the conclusions that can be drawn from various models.

Class Materials (Required)

TBA

Class Notes

IMC Full Time Students Only; Prereq: IMC 460.

Class Attributes

Attendance at 1st class mandatory

Enrollment Requirements

Enrollment Requirements: PreReq: IMC 460 AND IMC 461