AINS graduate course

AINS6002 · Machine Learning & Predictive Modeling

Core sequence · catalog 2026–27

Description

Core graduate ML: supervised and unsupervised methods, validation design, and responsible deployment patterns for prediction tasks.

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Syllabus outline

  1. Modules 1–2 · Foundations

    • Losses, optimization, regularization
    • Cross-validation and leakage
    • Baselines and error analysis

  2. Modules 3–4 · Methods

    • Tree ensembles and calibration
    • Clustering and dimensionality reduction
    • Feature engineering discipline

  3. Modules 5–6 · Practice

    • Imbalanced data and cost-sensitive learning
    • Monitoring drift and maintenance
    • Case studies from partner domains