본 강의에서는 머신 러닝의 공정성과 편향에 대한 근본적인 문제를 배웁니다. 대학교 입학부터 대출 허가까지, 예측 모델을 이용해 만드는 중요한 결정이 늘어나면서, 모델이 불공정한 예측을 만들지 않게 하는 것이 굉장히 중요해졌습니다. 인간에서 비롯된 편향부터 데이터 세트 인지까지, 윤리적인 모델 구축의 여러 방면을 알아보겠습니다.
인공 지능 데이터 공정성 및 편향
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
6 hours to complete
3 weeks at 2 hours 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
강의에 오신 여러분, 환영합니다! 1주 차에는 머신 러닝의 맥락에서 공정성이 무엇을 의미하는지, 여러 시나리오에서의 참 패리티가 무엇을 의미하는지 이야기합니다.
What's included
5 videos2 readings3 assignments2 discussion prompts
이번 주에는 불공정에 맞서볼 겁니다. 공정성 문제를 이해했으니, 공정성을 위반하지 않는 모델을 구축하는 방법을 알아봅시다.
What's included
5 videos2 readings3 assignments1 discussion prompt
이번 주에는 데이터 수집 및 속성 선택 과정에서 일어나는 편향 중 인간에서 비롯된 편향을 알아보겠습니다. 목표는 모델을 구축하기 전에 편향을 제거하는 겁니다.
What's included
5 videos2 readings3 assignments1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Algorithms
Siemens
University of Alberta
DeepLearning.AI
New York Institute of Finance
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