Predictive Modeling with Python
April 21, 2017
9:00am - 5:00pm
DBH 4011
Instructors: Preston Hinkle, John Schomberg
TAs: Gabe Yu
Introduction
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.
Contact: Please mail thinkle@uci.edu or jschombe@uci.edu for more information.
Tentative Schedule
| Time | |
|---|---|
| 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 |
| 12:30-1:00 | Lunch |
| 1:00-2:30 | Out of Sample Prediction |
| 2:30-2:45 | Break (coffee) |
| 2:45-4:30 | Logistic Regression |
Syllabus
- Introduction/Preparation
- Numpy
- Pandas
- Linear Regression
- Model
- GD Solution
- OLS Solution
- Out of Sample Prediction
- Cross validation
- Regularization
- Logistic Regression
- Model
- GD Solution
- Newton’s method
- More Applications
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Pre-Workshop Instructions
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