Machine Learning / Models and controlled selection

Machine Learning β€” Models and controlled selection

Optimization, trees, ensembles, clustering, dimensionality reduction, and cross-validation.

6 lessons Β· No account required

What you will learn

  • Optimization, trees, ensembles, clustering, dimensionality reduction, and cross-validation.

  1. Optimization and regularization

    Connect a fitted objective to gradient updates, numerical behavior, and controlled model complexity.

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  2. Trees and partitioning

    Understand splits, leaf predictions, and capacity controls without treating a fitted tree as an automatic explanation or probability guarantee.

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  3. Ensembles and error diversity

    Understand averaging, bagging, and boosting as distinct procedures whose benefit depends on member errors and valid selection.

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  4. Clustering and distance

    Explore similarity under a declared representation and evaluate clusters without pretending that unsupervised groups are true categories.

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  5. Dimensionality reduction and selection

    Distinguish a compact representation from a supervised feature choice, and keep both fitted inside the learning boundary.

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  6. Cross-validation and tuning

    Separate training, selection, and final assessment while treating preprocessing, thresholds, and stopping as parts of the chosen procedure.

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