Advanced Predictive Modeling with Python

Learn more about the practice of predictive modeling using Python.

Description:

Learn more about the practice of predictive modeling using Python.

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This course builds upon Predictive Modeling with Python, covering best practices for building predictive models that perform well in noisy, real-world domains. Feature learning/engineering and model ensembling will be the focus of the course. Examples and exercises will be implemented using iPython notebooks and the SciKit-Learn library. Participants must have taken Predictive Modeling with Python or obtain instructor permission to enroll.

This course is targeted primarily at graduate students who have not already taken a full course in machine learning.

Date: March 2, 2017

Time: 9 a.m. to 5 p.m. with lunch provided

Location: Donald Bren Hall, Room 4011

Instructors:Eric Nalisnick

Prerequisites: Predictive Modeling with Python or equivalent. To understand the more theoretical aspects of the course, it is recommended to have knowledge of linear algebra, probability, and calculus.

For course materials and more information, check out the

soon-to-be-updated Workshop page.