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.
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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
Coursera Project Network
Korea Advanced Institute of Science and Technology(KAIST)
Coursera Project Network
Korea Advanced Institute of Science and Technology(KAIST)
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