2 or 3 credits
Fall 2025 Lecture Upper Division
This is an introductory course in statistical and machine learning. It will cover fundamental concepts and essential tools that are critical in understanding cutting-edge machine learning techniques. Students will develop skills in applying a wide variety of modeling and prediction methods. Topics include linear regression, classification, regularization and shrinkage methods, tree-based methods, and support vector machines. An integral part of this course is the extensive use of the open source statistical software R. Students will gain hands-on experience in analyzing datasets commonly used in business and economics.
Learning Outcomes1Develop skills in applying a wide variety of modeling and prediction methods. Topics include linear regression, classification, regularization and shrinkage methods, tree-based methods, and support vector machines. An integral part of this course is the extensive use of the open source statistical software R.
2Gain hands-on experience in analyzing datasets commonly used in business and economics.
Restrictions
made by Purdue students.