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.
Artificial Intelligence Privacy and Convenience
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Artificial Intelligence Privacy and Convenience
This course is part of Ethics in the Age of AI Specialization
Instructor: Brent Summers
6,570 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.6
(77 reviews)
Intermediate level
Some related experience required
5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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Shareable certificate
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Assessments
9 assignments
Taught in English
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This course is part of the Ethics in the Age of AI Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning
What's included
5 videos2 readings3 assignments2 discussion prompts
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
What's included
4 videos2 readings3 assignments1 discussion prompt
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
What's included
5 videos1 reading3 assignments1 discussion prompt
Instructor
Instructor ratings
4.4 (17 ratings)Offered by
Recommended if you're interested in Data Analysis
The University of Notre Dame
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