April 21, 2017
9:00am - 5:00pm
Instructors: Preston Hinkle, John Schomberg
TAs: Gabe Yu
Python is a popular language for scientific computing and machine learning. This course will introduce general modeling concepts via exercises in which participants implement regression models in raw python and via the scikit-learn library. Focus will be given to model fitting and evaluation. The course will be taught mostly through the medium of Jupyter notebooks.
Who: This course is targeted primarily at graduate students and researchers who have not already taken a full course in machine learning.
Requirements: Participants must bring a laptop with a few specific software packages installed (see Pre-Workshop Instructions).
Prerequisites: A previous course in programming is strongly recommended.
|8:30-9:00||Sign-in (coffee & bagels)|
|9:00-10:30||The IPython Notebook and Pandas|
|10:30 - 10:45||Break|
|10:45-12:30||Linear Regression and Predictive Modeling|
|1:00-2:30||Out of Sample Prediction|
See the course’s GitHub page for instructions: https://github.com/UCIDataScienceInitiative/PredictiveModeling_withPython/tree/sp_17 We’ll expect you to have the Anaconda Python distribution installed with version 2.7 activated: https://www.continuum.io/downloads