Introduction

What is Machine Learning?

• E = the experience of playing many games of checkers
• T = the task of playing checkers.
• P = the probability that the program will win the next game.

In general, any machine learning problem can be assigned to one of two broad classifications: Supervised learning and Unsupervised learning.

Supervised Learning

• Regression Problem: 연속적인 값을 예측
• Classification Problem: 이산 적인 값으로 구분(Boolean, Integer)
• Support Vector Machine: handle large feature space

Unsupervised Learning

• Clustering: i.g. 구글 뉴스, 컴퓨터 클러스터 구성, 사회 관계망 분석
• Cocktail party problem: 두 사람의 목소리 분리하기 혹은 노이즈 제거하기

Linear regression with one variable

Model and Cost function

Input

$$ x_i $$

Output

$$ y_i $$

Training set