Machine Learning Series

This series invites a hands on experience on how Machine Learning models can be applied within social science research. Best suited for social scientists with working Python proficiency and quantitative research experience. 

Courses:

• Machine Learning in the Social Sciences (1/17, 10 - 11 AM): Overview on machine learning models and how it is currently being used in social science research.  
• Exploratory Data Analysis for Machine Learning (1/17, 11:15 AM - 12:45 PM): Gain hands-on experience with data exploration and processing techniques consistently used within projects. 
• Supervised Learning (1/17, 1:30 - 3:00 PM): Walk through building a supervised classifier model from data preparation, model selection, to generating predictions.
• Unsupervised Learning (1/18, 10 - 11:30 AM): Explore and represent emergent patterns within data by developing your own unsupervised Machine Learning models.
• Natural Language Processing (NLP) (11/18, 11:15 AM - 12:15 PM): Dive into the intersection of ML and text data through constructing both supervised and unsupervised NLP models.

Familiarity using Python is strongly encouraged. Suggested to view the video recordings for our Fall 2022 introductory Python Bootcamp(Day 1, Day 2). 

There are many excellent Python tutorials through Linkedin Learning. Cornell users have a free membership.

Registration is mandatory. Register here.

Sessions will be held hybrid in person and virtual. In person location included below.

Mann Library B30B, 237 Mann Dr, Ithaca, NY 14853. Marked number 4 in Floor plan.

If attending virtually zoom details will be sent after registering.