Modèles d'Apprentissage Machine

Notions Clés

• Observation: Real-world measurements used for decision-making.
• Similarité: Estimated measure of resemblance between examples; not equivalent to distance.

Types de Modèles

Modèles Linéaires

• Models based on linear relationships between input and output variables.
• Limited to linear phenomena.

Modèles Non-Linéaires

• Adapt to complex phenomena.
• Employ more flexible structures for categorising observations.

Fonction Noyau

• Used in models like SVM and ANN to transform non-linear problems into linear ones in another space.

Approches en Classification

• Non-Paramétriques:

  • Based on example similarity.
  • Don't assume anything about data distribution.

• Probabilistes:

  • Estimate probability distributions from data.
  • Quantifies decision uncertainties.

Méthodes d'Apprentissage

Supervisé

• Training with labelled data.
• Aims to minimise prediction errors against observed results.

Non-Supervisé

• Data grouping into homogenous clusters without labels.

Par Renforcement

• Learning by rewarding/punishing actions to maximise accumulated reward.

Semi-Supervisé

• Combines labelled and unlabelled examples, exploiting data relationships.

Modèles Courants

• Arbre de Décision: Simple, interpretable; unstable with minor data changes.
• SVM (Support Vector Machine): Separates classes by maximising margin; uses kernel functions for non-linear cases.
• Réseaux de Neurones (MLP, CNN, etc.): Powerful for complex tasks but require large datasets.
• k-NN (k-plus Proches Voisins): Classifies examples based on nearby data points; non-parametric model.

Applications des Méthodes d'IA

Classification

• Includes facial, voice, handwriting recognition and autonomous vehicle object detection.

Régression

• Used in predicting time series (finance, weather), and physical phenomena.

Clustering

• Forms homogenous groups, such as image segmentation and user profiling.

Réduction de Dimension

• Simplifies data, improving calculation time and precision.

Évaluation des Modèles

• Validation Croisée: Divides data into subsets for training/testing.

  • Example: k-folding.

• Mesures de Performance:

  • Précision: Correctly identifying positive classes.
  • Rappel: Identifying all positive classes.
  • Courbes ROC: Evaluates model performance by considering true positives versus false positives.

Conclusion

• Model selection depends on data type, problem characteristics and complexity (linear/nonlinear).
• Modern tools, like neural networks, are powerful but can lack clear explanation.
• Rigorous interpretation and validation are crucial for model effectiveness and ethical considerations.

Ce quiz explore les différents types de modèles en apprentissage machine, y compris les modèles linéaires et non-linéaires. Il aborde également les approches de classification, tant non-paramétriques que probabilistes, et les méthodes d'apprentissage supervisé. Testez vos connaissances sur ces concepts clés en décision et en classification.