> For the complete documentation index, see [llms.txt](https://sdc-james.gitbook.io/onebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sdc-james.gitbook.io/onebook/1.1.-artificial-intelligence/1.7..md). # 1.6. 머신러닝 알고리즘 \ 머신러닝은 제공되는 데이터를 기반으로 컴퓨터를 학습시켜서 무언가를 예측하게 만드는 기법입니다. 즉 컴퓨터에게 여러가지 예제 데이터를 주고 스스로 학습하여 프로그램을 작성하도록 하자는 것입니다. 머신 러닝 알고리즘은 크게 세가지 분류로 나눌 수 있습니다. 바로, 지도 학습(Supervised Learning), 비지도 학습(Unsupervised Learning), 강화 학습(Reinforcement Learning)입니다. ![http://solarisailab.com/archives/1785](/files/-LbN21NtMJTyPW1Or74N) 머신러닝, 딥러닝의 이론적인 이해를 하려면 김성훈님의 Youtube “모두를 위한 딥러닝” 강좌를 반드시 공부하기를 바랍니다. 강의: 소스: 정리: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://sdc-james.gitbook.io/onebook/1.1.-artificial-intelligence/1.7..md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.