L'apprentissage automatique compte trois types principaux — l'apprentissage supervisé (apprentissage à partir d'exemples étiquetés), l'apprentissage non supervisé (détection de motifs dans des données non étiquetées), et l'apprentissage par renforcement (apprentissage par essai et récompense). Les comprendre clarifie comment le ML aborde différents problèmes.
Les trois principaux types
SUPERVISED LEARNING → learn from LABELED data (input → known correct output):
→ trained on examples with answers → learns to predict outputs for new inputs
→ for: classification (categorize), regression (predict numbers)
→ e.g. spam detection (labeled spam/not-spam), price prediction
UNSUPERVISED LEARNING → find patterns in UNLABELED data (no given answers):
→ discovers structure/groupings on its own
→ for: clustering (group similar items), dimensionality reduction, anomaly detection
→ e.g. customer segmentation, finding patterns
REINFORCEMENT LEARNING → learn through TRIAL and ERROR with REWARDS:
→ an agent takes actions, gets rewards/penalties, learns to maximize reward over time
→ for: game playing, robotics, control, decision-making
