Course content

Data Mining

Année: 2025-2026

Course Content

  1. Classical Data Mining (Sections 1–7)
    • 1. Data Mining
    • 1.1. Patterns in Data
    • 1.2. Machine Learning Approaches
    • 1.3. Data Mining Activities
    • 2. Data Representation and Formalization
    • 2.1. Data Preparation
    • 3. Classification
    • 3.1. Support Vector Machine (SVM)
    • 3.2. K-Nearest Neighbors
    • 3.3. Naive Bayes Classification
    • 3.4. Decision Trees
    • 3.5. Ensemble Learning (Random Forest)
    • 4. Regression
    • 4.1. Stochastic Gradient Descent
    • 5. Clustering
    • 5.1. Clustering Algorithms (K-Means, Hierarchical, DBSCAN)
    • 6. Anomaly Detection
    • 6.1. Anomaly Detection Algorithms
    • 7. Feature Selection
    • 7.1. ML Workflow
    • 7.2. Problem Formulation
    • 7.3. Train / Validation / Test Partitions
    • 7.4. Cross-Validation
    • 7.5. Summary
  2. Machine Learning and Deep Learning (Section 8)
    • 8.1. Neural Network Fundamentals
    • 8.2. Deep Learning
    • 8.2.1. Training: Optimization (SGD, Adam)
    • 8.2.2. Training Stability
    • 8.3. Scientific Data Modalities
    • 8.3.1. Time Series and Signals
    • 8.3.2. Images and Spatial Detectors
    • 8.3.3. Tabular Data
    • 8.4. Reinforcement Learning
    • 8.5. Ethics, Licenses and Privacy
    • 8.6. Uncertainty and Calibration
    • 8.6.1. Uncertainty Estimation
    • 8.6.2. Robustness

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