The decomposition with the actual math, the three terms and what drives each, why the modern interview asks it as a sizing question (“which hyperparameter first?”), and diagnosing under/overfitting from a learning curve instead of a single number.

• →Generalization Diagnosis

Why the loss is the algebraic commitment: MSE for real targets, cross-entropy for classes and why (convex Hessian, non-saturating gradient). Then the dynamics — batch vs SGD, Adam’s per-coordinate scaling, when Adam hurts generalization, and the learning-rate regimes.

• →Loss and Optimization

The variance dial: L1/L2/elastic-net geometry and when each wins, dropout’s implicit ensemble, early stopping as spectral regularization. Then the orthogonal problem — why probabilities miscalibrate, Platt vs isotonic (the 1000-sample rule), and ECE/reliability diagrams.

• →Regularization and Calibration

The decision is a function of data shape, volume, latency, interpretability, and cost — not a leaderboard. Logistic regression vs gradient-boosted trees vs neural nets vs LLMs, with named case studies where the “smaller” model won, and the real cost/latency numbers.

• →Model Selection and Evaluation

Constraints first, metric second. The accuracy paradox, precision/recall/F1, ROC-AUC vs PR-AUC on imbalance and why the choice flips, thresholds via calibrated cost (not Youden), and the silent killer — data leakage, with the named case studies and the CV protocols that prevent it.

• →Model Selection and Evaluation
• →Generalization Diagnosis

A timed rapid-fire gauntlet across the whole track — bias-variance, loss/optimization, regularization, calibration, model selection, metrics, leakage. Practitioner framing (process over answer), the khangich/alirezadir question banks, and five scenario checkpoints under pressure.

• →Generalization Diagnosis
• →Loss and Optimization
• →Model Selection and Evaluation