coursera의 Supervised Machine Learning: Regression and Classification 강좌 내용입니다.
비지도 학습
input x가 필요하지만 label y는 사용하지 않는다.
알고리즘은 데이터에서 구조를 찾는다.
비지도 학습 - clustering(군집)
비슷한 데이터끼리 그룹핑한다.
비지도 학습 - Anomaly detection(변칙 처리)
비정상적인 데이터(움직임)을 감지
비지도 학습 - Dimensionality reduction(차원의 축소)
데이터를 더 적은 수로 압축시킨다.
Q. Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.)
1. Given a set of news articles found on the web, group them into sets of articles about the same stories.
2.
Given email labeled as spam/not spam, learn a spam filter.
3. Given a database of customer data, automatically discover market segments and group customers into different market segments.
4. Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.
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