In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. Diving into the ethics behind these decisions, we will explore how to protect users from privacy violations while creating useful predictive models. We will also ask big questions about how businesses implement algorithms and how that affects user privacy and transparency now and in the future.
Overview
Syllabus
- Privacy and convenience vs big data
- In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning
- Protecting Privacy: Theories and Methods
- In Module 2, we are going to take a deeper look at dataset security. We will also look into methods to add privacy to existing and new datasets to protect those individuals in them
- Building Transparent Models
- In Module 3, we will discuss putting ethical, private models into practice. We will explore the explainable AI movement as well as tradeoffs for the teams putting together these algorithms
Taught by
Sabrina Moore and Brent Summers
- Coursera
- Free
- English
- Certificate Available
- Available at any time
- intermediate
- French, Portuguese, Russian, English, Spanish, Arabic, German, Thai, Indonesian, Kazakh, Hindi, Swedish, Korean, Greek, Italian, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish