Name: An Introduction to Differential Privacy as Applied to Machine Learning
Start: 2015-12-02T19:00:00-08:00
End: 2015-12-02T21:00:00-08:00
Location: Metis San Francisco

We have a very exciting talk this month by Dr. Nina Zumel (http://ninazumel.com/), a principal consultant and co-founder at Win-Vector LLC (http://win-vector.com/), a data science consulting firm based in San Francisco. She is also the co-author, with Dr. John Mount (http://www.win-vector.com/site/staff/john-mount/), of Practical Data Science with R (http://www.manning.com/zumel/) and a contributor to the popular Win-Vector blog (http://www.win-vector.com/blog/). When she isn’t working, she also likes to dance and read folklore and ghost stories.

Differential privacy (https://en.wikipedia.org/wiki/Differential_privacy) was originally developed to facilitate secure analysis over sensitive data. It’s back in the news again now, with exciting (http://arxiv.org/abs/1411.2664) results (https://www.sciencemag.org/content/349/6248/636.abstract) from Cynthia Dwork, et. al. that apply results from differential privacy to improve machine learning performance. Nina will give a brief introduction to the ideas behind differential privacy, and review how differential privacy can be used to enable safer re-use of holdout data in machine learning. She will also show how differential privacy can be used to improve effects coding: that is, how you can build efficient encodings of categorical variables with many levels, without introducing additional bias into modeling procedures.

Over the past month or so, the Win-Vector blog (http://www.win-vector.com/blog/) has featured a series of posts on differential privacy (http://www.win-vector.com/blog/2015/11/our-differential-privacy-mini-series/), if you want to learn more. The Win-Vector blog is one of my favorite applied machine learning / data science blogs. It offers clear, yet very in-depth technical articles on a number of interesting and practical data science topics. It's not just "yet another data science blog"... :)

https://ninazumel.files.wordpress.com/2015/08/ninaheadshot-e1440451581459.jpg?w=502&h=488

The meetup will be hosted at the new SF office for Metis (http://www.thisismetis.com/), which offers project-based data science bootcamps and professional development training.

We will also have pizza, thanks for support from Metis!

Erin LeDell

Bay Area Women in Machine Learning & Data Science

Technology

Computer Programming

Big Data

Machine Learning

Data Analytics

Predictive Analytics

Data Mining

Data Science

WiMLDS

An Introduction to Differential Privacy as Applied to Machine Learning

Metis San Francisco

Share

Bay Area Women in Machine Learning & Data Science

An Introduction to Differential Privacy as Applied to Machine Learning

Bay Area Women in Machine Learning & Data Science

Details

Members are also interested in