It's meetup time, again and Zendesk is inviting us to their office, once again.

Agenda:

7pm: Meet & Greet

7:30pm: "Python Web Scraping Tools: A Survey" by Jon Reiter

There are myriad web scraping tools available in Python spanning a broad range of use cases. At the same time there are many surprising gaps in coverage. Further complicating matters, differences which look innocuous in a browser can have an outsized impact on the design of an automated browsing system. In this talk we survey a collection of common web scraping frameworks and work out a mapping from real-world use cases to packages. Along the way we address common questions like:

How do I choose among content parsers? What if a page is dominated by JavaScript or HTML5? If I'm going to control a browser which one should I choose? Can I run this in the cloud with no access to a display? Can I download files?

About Jon:
Jon worked in finance as a derivative trader for a long time before moving back to technology. He is now heading a start-up which aims to re-cast and modernize the way the financial services industry processes market data.

8:00pm: An Introduction to Private Machine Learning by Satish Shankar

This talk will introduce the essential concepts from cryptography necessary to build AI systems that use sensitive data and yet protect our privacy. Specifically, we will cover concepts from secure multi-party computation (MPC) and how they can be used to build machine learning algorithms.

Why does this matter? This matters because we as a society are struggling to balance the benefits of data driven systems and the privacy risks they create. Building any machine learning or analytics model necessitates the collection of data. If this data is sensitive or personal, it inevitably turns into an honeypot for hackers. At a societal level, we are responding to this issue by introducing more regulation such as the GDPR.

Instead of regulations, it is possible to use cryptography to protect our data and still analyse it: This talk show how.

About: Shankar leads the machine learning and AI efforts for Manulife’s innovation labs. He works on quantitative investment and insurance, drawing on a wide range of fields from machine learning, natural language processing, differential privacy, encryption, and more.

He is particularly interested in the intersection of blockchains, distributed systems and privacy in machine learning.