Computer Science / Artificial Intelligence

Artificial Intelligence Privacy and Convenience

Brought by: Coursera

Overview

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.

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

Computer Science / Artificial Intelligence

Artificial Intelligence Privacy and Convenience

Artificial Intelligence Privacy and Convenience

Brought by: Coursera

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

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