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Principles and Structure of AI: ③ How Does AI Learn and Change?

👉 This course is ideal for those who want to properly understand how AI changes and its limitations. Is AI finished once it’s created? This course explains how AI changes through learning and why the results can differ even with the same AI. It clearly explains the meaning of the learning process and the technical reasons why performance may improve or decline.

1 learners are taking this course

Level Beginner

Course period Unlimited

AI
AI
Python
Python
Deep Learning(DL)
Deep Learning(DL)
LLM
LLM
RAG
RAG
AI
AI
Python
Python
Deep Learning(DL)
Deep Learning(DL)
LLM
LLM
RAG
RAG

What you will gain after the course

  • Understanding what AI learning means

  • Structurally explain why performance differs

  • Recognizing both the possibilities and limitations of AI

How does AI think and answer?

Mastering AI Principles and Architecture from the Ground Up Series


AI Principles and Structure ③: How AI Learns and Changes

1. Raw Data Input

2. AI Learning

3. Better Decisions

4. Pattern discovery, error correction, experience accumulation, and performance improvement


What is the purpose of creating this course, and what are its applications?

1) Purpose of creating the lecture

  1. I’m curious why AI needs to learn.

  2. It explains the process of learning patterns through data and improving performance.

  3. We clearly summarize how the process changes by reducing errors and making better decisions.

  4. It helps beginners understand and apply the learning principles of AI.

2) Applications

Spam detection, sentiment analysis, speech recognition, anomaly detection, image classification, recommendation systems, autonomous driving, and smart factories


This course explains things you have always been curious about in an easy-to-understand way.

What You’ll Learn


Notes Before Taking the Course

Things to Know Before Enrolling

  • Please note that the knowledge sharer personally handled the work from initial planning through the creation of the course materials.

  • To enhance the quality of the course, please note that generative AI was used as a supplementary tool.

  • The voice included in the video is the knowledge sharer's own voice, which has been modified and edited.

Learning materials

  • Format of the provided learning materials (PPT, cloud links, text, source code, assets, programs, sample problems, etc.)

  • Length and file size, as well as other characteristics and notes regarding the learning materials】【。

Prerequisite Knowledge and Important Notes

  • Whether prerequisite knowledge is required, considering the difficulty level of the course

  • Content directly related to taking the course, such as lecture video quality (audio/video), and recommended study methods

  • Questions/answers and information related to future updates

  • Notices Regarding Copyright of Lectures and Learning Materials

Recommended for
these people

Who is this course right for?

  • People who want to know how AI changes through learning

  • Those curious about why the results differ even with the same AI

  • Those who want to understand both AI performance and its limitations

Need to know before starting?

  • ①, ② Lecture content (optional)

  • A Basic Understanding of AI Learning Concepts

  • No coding or formula comprehension is required.

Hello
This is djshin08317101

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Curriculum

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11 lectures ∙ (3hr 57min)

Course Materials:

Lecture resources
Published: 
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