알고리즘의 영향력은 점차 커지고 있습니다. 머신 러닝은 방대한 데이터 세트를 기반으로 주요 의사결정을 내리기 시작함에 따라, 인간인 우리는 실제 생활에서의 한계점을 충분히 알고 있어야 합니다. 대출 승인, 교통 경로 재설정을 막론하고, 머신 러닝 모델에는 인간의 공유가치가 정확히 반영되어야 합니다. 본 강좌에서는 가장 기본적인 알고리즘부터 완전 자율 알고리즘에 이르기까지 알고리즘의 발전을 살펴보고, 보다 윤리적으로 건전한 알고리즘을 만드는 방법을 논의합니다.
AI 알고리즘 모델과 한계점
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4.3
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Beginner level
No prior experience required
7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
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Assessments
12 assignments
Taught in Korean
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There are 4 modules in this course
수강생 여러분 환영합니다! 강좌 구조에 대한 개요를 소개한 뒤, 본격적으로 알고리즘의 세계를 살펴봅니다.
What's included
5 videos2 readings3 assignments2 discussion prompts
이번 주 강좌에서는 이론과 실제의 핵심 차이점인 예측 모델링을 자세히 알아봅니다.
What's included
3 videos2 readings3 assignments1 discussion prompt
이번 주 강좌에서는 보다 정확하고 윤리적인 모델을 모색하기 위해 머신 러닝 정확도와 학습 가이드라인을 집중적으로 살펴봅니다.
What's included
3 videos2 readings3 assignments1 discussion prompt
마지막 주 강좌에서는 이 모든 예측 지능이 어디로 향하는지 이에 대한 몇 가지 중요한 화두를 던져 봅니다. AI가 지나온 길과 사회에 미치는 광범위한 영향에 대해 논의합니다.
What's included
4 videos2 readings3 assignments1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Data Analysis
Lund University
Intel
Emory University
SAE Institute México
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