Predictive Modeling with Python

Learn about the use of predictive models in Python through scikit-learn.

Python is a popular language for scientific processing and machine learning. This course will introduce general modeling concepts in addition to concrete examples based on the scikit-learn library. Example usage of scikit-learn will illustrate how to fit and evaluate predictive models. Regression and classification settings will be considered. The course will be taught mostly through the medium of iPython notebooks.

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

Date: November 7, 2017

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

Location: Donald Bren Hall, Room 3011

Instructor: Preston Hinkle, Anna Kwa, UC Irvine

Pre-requisites: basic familiarity with Python (prior experience with scikit-learn is not necessary). To understand the more theoretical aspects of the course, it is recommended to have knowledge of linear algebra, probability, and calculus.

Learning Materials:https://github.com/UCIDataScienceInitiative/PredictiveModeling_withPython