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When you understand data, AI becomes easy.

Is AI difficult because of complex algorithms? Actually, the real power of AI comes from 'data'. You need to understand the essence of data to properly handle AI. This is a lecture that explains the roots of AI from a data perspective, covering everything from basic philosophy to perceptrons, CNNs, and data processing and ethics.

3 learners are taking this course

  • aidercollege
AI 활용법
알고리즘
데이터분석
인공지능
데이터전처리
Digital Literacy
Algorithm
AI
Data literacy
ChatGPT

What you will learn!

  • Artificial intelligence is not just technology, but a tool for thinking.

  • People who use AI well are people who handle data well.

💡 What drives AI is not algorithms, but data.

The real power of AI comes from 'data'.

Many people approach artificial intelligence only as technology and quickly give up.
However, to properly understand AI, you need to know the essence and thinking methods of data first.


This course covers everything from basic philosophy to perceptrons, CNN, and data preprocessing and ethics
the first artificial intelligence literacy course that explains the roots of AI from a data perspective.

Features of this course

📌Clear Summary of Two AI Thinking Approaches
→ Easily explains Symbolism vs Connectionism, Cognitivism vs Computationalism from philosophical and mathematical perspectives.

📌Understanding the Principles of Perceptrons, CNNs, and Image Recognition
→ Provides a practical explanation of where modern AI originated, its structure, and limitations.

📌 Acquire core practical AI knowledge centered on data
→ Learn real-world industry content from data collection and preprocessing to bias issues.

📌 Data Terminology Reference for Immediate Use in the Field

→ We'll organize confusing concepts like annotation, visualization, and labeling all at once.

I recommend this for people like this

Office workers and marketers who find AI overwhelming

Those who want to easily understand concepts and apply them immediately

Developer Preparing to Transition to AI Field

Those who want to build a solid foundation that will lead to practical work

Educators who need to explain and plan AI

Someone who needs to give lectures or do planning in the field

After taking the course

  • You can explain the various thinking approaches of AI (symbolism and connectionism).

  • You can clearly understand the principles and limitations of perceptrons and CNNs.

  • You can acquire practical data processing capabilities including data preprocessing, annotation, and bias issues.

  • You can grow into an AI utilization expert with data literacy.


Introduction Question

Recognize key issues and context in advance, and establish a reference point for thinking that allows you to follow the flow of the lecture.

Key Keyword Summary

We organize key concepts that allow you to review what you've learned at a glance.

Using Visual Materials

I structured it so you can intuitively understand actual research graphs, experimental photos, and various concepts.

Course Wrap-up

A summary screen that organizes the key content for each course is provided, allowing for natural review and organization.




The person who created this course

AIder College

Professor Jang Byeong-cheol

  • Professor Jang Byeong-cheol (Hanyang University)

  • Former National AI Curriculum Development Committee Member

  • Former EBS AI Instructor

  • 📖 Books: 《Artificial Intelligence Fundamentals》, 《Artificial Intelligence and Future Society》

👉 A professor who has experienced both actual education and research will teach you directly.

Recommended for
these people

Who is this course right for?

  • Non-majors and working professionals new to AI

  • A beginner developer preparing to transition into the AI field

  • Teachers, professors, and developers who need to explain AI

Hello
This is

Aider College는 ‘AI를 친근하게, 쉽게, 제대로’ 가르치는 AI교육전문 브랜드 입니다.

서울대, KAIST, 한양대, 서울여대 등 국내 최고 교수진이 직접 참여하여, 기술 + 철학 + 윤리 + 인사이트를 모두 담은 독창적인 커리큘럼을 통해 AI를 제대로 이해하고 활용하는 시민(Aider)을 양성합니다.
AI 시대, 단순한 사용법을 넘어 생각하고 판단할 수 있는 AI 역량을 함께 키워갑니다.

Curriculum

All

20 lectures ∙ (5hr 20min)

Published: 
Last updated: 

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