Description

Examine new techniques for predictive and descriptive learning using concepts that bridge gaps among statistics, computer science, and artificial intelligence. This course emphasizes the statistical application of these areas and integration with standard statistical methodology. The differentiation of predictive and descriptive learning will be examined from varying statistical perspectives.

Prerequisites

• Complete Data Mining and Analysis (Stanford Course: STATS202), Introduction to Statistical Learning (Stanford Course: STATS216) OR Introduction to Applied Statistics (Stanford Course: STATS191)
• A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.

Topics include

• Classification & regression models
• Multivariate adaptive regression splines
• Prototype & near-neighbor methods
• Neural networks

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.