ML Paradigms Simulator

🎓

Supervised Learning

LABELS: YES ✓

Learning with a Teacher: The agent has a complete map with explicit directions at every intersection.

📋 Teacher's Instructions:

Waiting to start...

Steps Taken: 0

Status: Ready

In supervised learning, every piece of training data has a label (the correct answer). The model learns to map inputs to outputs directly.

🔍

LABELS: NO ✗

Self-Discovery: The agent explores alone with no map, gradually discovering the structure.

🗺️ Discovery Log:

No discoveries yet...

Explored: 0%

Patterns: 0

Steps: 0

In unsupervised learning, there are no labels. The algorithm must find hidden structures and patterns in raw data on its own.

🎮

LABELS: PARTIAL ~

Trial and Error: The agent learns through rewards and penalties. No explicit instructions.

📊 Reward Feedback:

🤔

🍫

Rewards

0

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Penalties

0

Episode: 1

Steps: 0

Best: -

Reinforcement learning uses partial feedback through rewards. The agent learns by trial and error to maximize rewards.

Aspect	🎓 Supervised	🔍 Unsupervised	🎮 Reinforcement
Data Labels	YES ✓	NO ✗	PARTIAL ~
Learning Style	With a teacher	Self-discovery	Trial & error
Feedback Type	Correct answers	Pattern detection	Rewards/penalties
Example Uses	Image classification	Clustering	Game AI