ML 강의 1-3강 정리

📌 ch 1 . Machine Learning

🧐 machine Learning이란

Key ingredients in ML

• Data
  • Structured or unstructed data
  • IID(Independent identically distributed) data or non-IID data
• Model
• Training(algorithm & inference)
  • Nonparametaric models
  • Parametric models

🧐 Algorithm 이란

1. task 주어지고
2. trainning experience 즉, data가 주어졌을때
3. a performance measure이 되는 것

🧐 ML과 NON-ML의 차이

Re Programming을 하는지 Re-Training을 하는지

🧐 Machine Learning Taxonomy

	Feedback	Goal
Supervised Learning	명시적인 피드백	Regression&Classification
Unsupervised Learning	No feedback	Representation learning & clustering
Reinforcement Learning	Evaluative feedback(시스템이 피드백을 제공)	sequential decision making

즉, self-supervised는 unsuperviesed에 포함
supervised : labeld data
unsupervised : unlabled data
Reinforce learning : 다양한 상황을 스스로 학습함

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📌 ch 2 . Machine Learning for Regression

OLS and MLE

Regression and Linear models