Course Description
The goal of this course is to introduce students to a wide range of methods, which are designed to tackle commonly seen real-world problems, and are intensively used in the current literature. These methods include linear regression, logistic regression, bootstrapping, cross validation, bagging, boosting, splines, random forests, neural networks, and support vector machines. This course is application oriented. We will emphasize the intuition behind each method and touch on a little bit of theory.
Fall 2024
Instructors
Meeting Patterns
Classes Start:
August 19, 2024
Classes End:
December 3, 2024
Location:
01206 Nelson Hall
Class Days:
M W
Class Start Time:
11:45am
Class End Time:
1:00pm
Class Type:
Lecture
Credits:
3.00
Restrictions:
R: Graduate Students Only
