[강의]시즌1 딥러닝의기본 - 기본적인 Machine Learning의 용어와 개념

Basic concepts

* What is ML?

* what is learning?

-supervised

-unsupervised

* what is regression?

* what is classification?

Matching Learning

* Limitations of explicit programming

(개발자가 이런환경에서는 이렇게 작동하라. 라고 explicit 하게 한 경우)

(spam필터 기능들은 explicit 하지 않으므로 동작하기 어렵다.)

 - spam filter : many rules

 - Automatic driving : too many rules

* Machine learning : "Field of study that gives computers the ability to learn without being explicitly programmed" Arthur Samuel(1959)

Supervised / Unsupervised learning

* Supervised learning : 

- learning with labeled examples = training set.

an example training set for four visual categories.

cat , dog , mug , hat.

learning data that labeled like cat. 

* Unsupervised learning ; un-labeled data

- Google news grouping

- Word clustering

 Supervised learning

 * Most common problem type in ML

- Image labeling : learning form tagged images

- Email spam filter : learning from labeled ( spam or ham) email

- predicting exam score : learning from previous exam score and time spent

Training data set

label 된 data를 가지고 학습을함. 모델을 생성,

x 라는 data가 주어졌을 때 y 라는 값을 출력.

Training data set is essential.

AlphaGo

learning Go data.

Types of supervised learning

 * Predicting final exam score based on time spent

- regressions

wide range.

 * Pass/non-pass based on time spent   

- binary classification

 * Letter grade(A,B,C,E and F) based on time spent

- multi-label classification

Predicting final exam score based on time spent

x(hours)    y(score)

- regressions

Pass/non-pass based on time spent

- binary classification