본 강의에서는 머신 러닝 분야에서의 보안 및 프라이버시와 관련된 기본 개념을 살펴봅니다. 그 기저에 깔린 윤리를 깊이 있게 탐구하면서, 유효한 예측 모델을 구축하는 과정에서 사용자의 프라이버시를 보호하는 방법을 알아보겠습니다. 또한 두 가지 심층 질문을 통해, 기업이 알고리즘을 구현하는 방법과 그에 따라 현재와 미래에 사용자 프라이버시 및 투명성에 영향을 미치는 방법도 모색할 것입니다.
AI 프라이버시 및 편의성
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Details to know
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Assessments
9 assignments
Taught in Korean
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There are 3 modules in this course
모듈 1에서는 머신 러닝에서 익명성과 프라이버시의 실제 의미가 무엇인지 알아봅니다.
What's included
5 videos2 readings3 assignments2 discussion prompts
모듈 2에서는 데이터 세트의 보안에 대해 자세히 알아봅니다. 또한 기존 및 신규 데이터 세트에 포함된 개인을 보호하기 위한 프라이버시 보장 기술도 함께 살펴보겠습니다.
What's included
4 videos2 readings3 assignments1 discussion prompt
모듈 3에서는 윤리적 비공개 모델 실현에 대해 알아봅니다. 설명 가능한 AI의 발전 동향과 이러한 알고리즘을 개발하는 팀에서 겪을 수 있는 이해 충돌 문제를 함께 살펴보겠습니다.
What's included
5 videos1 reading3 assignments1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Data Analysis
Duke University
University of Virginia
Erasmus University Rotterdam
Duke University
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