AI & Machine Learning Basics

AI & Machine Learning Basics is the learning area for understanding the core concepts of artificial intelligence and machine learning. This section builds a systematic foundation — from the relationship between AI, ML, and deep learning — through to the practical knowledge needed to move into hands-on practice.

The Relationship Between AI, Machine Learning, and Deep Learning

These three concepts form a nested structure.

graph TD
    AI["AI (Artificial Intelligence)\nAll techniques that replicate human intelligence"]
    ML["Machine Learning\nAutomatic pattern learning from data"]
    DL["Deep Learning\nMulti-layer neural networks for automatic feature extraction"]
    AI --> ML
    ML --> DL

• AI (Artificial Intelligence): All techniques that replicate human-like intelligent behavior on a computer
• Machine Learning (ML): A subfield of AI — methods that automatically learn patterns from data
• Deep Learning (DL): A subfield of ML — methods that use multi-layer neural networks

In other words, “deep learning is one machine learning technique, and machine learning is one approach to achieving AI.”

What I Will Learn in This Section

What Is Machine Learning?

Covers the definition of machine learning and the fundamental difference from traditional programming. Learn the three types — supervised learning, unsupervised learning, and reinforcement learning — along with representative algorithms for each.

What Is Deep Learning?

Explains how neural networks work and why they are called “deep.” Covers application examples in image recognition and natural language processing, plus an overview of LLMs (Large Language Models).

Learning Paradigms

Covers modern practical learning techniques including transfer learning, fine-tuning, multitask learning, and federated learning. This page answers the question: “Why not train from scratch every time?”

Before Reading This Section

No special mathematical background is required. A basic grasp of high-school mathematics (functions and probability) will deepen understanding, but the conceptual content is accessible without it.

Next Steps

After grasping the basics, move on to specific algorithms and implementations.

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Link to this page (Japanese): AI・機械学習の基礎