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Software Analysis and Design - ML
학교 공부/소프트웨어 분석 및 설계

Software Analysis and Design - ML

2021. 12. 5. 01:01

1. Intelligence

1-1. Intelligence의 정의

1. The ability to learn or understand or to deal with new or trying situations

2. The ability to apply knowledge to manipulate the environment or to think abstractly as measured by objective criteria

 

1-2. Intelligence를 평가하는 척도

Scope of Learning

Intensity of Learning

Accumulation of Knowledge learned

 

 

2. Intelligent system

2-1. Intelligent system의 정의

A system that incorporates intelligence into applications. The intelligence is often delivered by learning capability.

A system that can imitate, automate some intelligent behaviors of human being.

A system that is based on approaches, methods or techniques of the Artificial Intelligence.

 

2-2. Intelligent system의 목적 (중요해보임)

Quality service : To perform more accurate and effective operations for solving the related problems

Adaptability : A system adapts itself to deal with changes in problems

 

2-3. Intelligent system의 방법론

ML 기술은 Intelligent system의 필수 요소이다.

 

2-4. Intelligent system의 특징 (중요해보임)

 

3. Learning

Learning이란 무엇인가? : Activity of understanding and memorizing a useful knowledge on some subjects

Learning의 가치는? : Ability of applying the learned knowledge in performing relevant activities

system의 knowledge :  Embedded Knowledge(프로그래밍 언어로 지식 표현) / Logics to Apply the Knowledge (프로그랭 구성을 사용하여 지식 표현)

 

 

4. ML

Learning by (Software) Machine : Machine learning is a software mechanism to acquire insights and knowledge from datasets(Learning) and make useful decisions(Prediction) based on the acquired knowledge

 

ML의 2개의 View : Training(Dataset을 이용해 학습한다는 점에서 일반 프로그램과 차이) / Prediction

 

5. ML for Intelligent system (중요해보임)

1. What is an Intelligent System? : A software system that embeds a rich knowledge and apply the knowledge in performing specified tasks in intelligent manners.

2. Embedding Knowledge with Machine Learning : Notion of Training Models

3. Applying the Knowledge with Machine Learning : Notion of Prediction with Trained Model

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