Learning Systems and Classical ML

Define the population, decision, loss, and evidence under which performance is expected to transfer.

Concept labFoundation90 min estimateArtifact: Generalization contract audit

Use information availability, time, entity, group, and provenance to construct honest evaluation partitions.

Failure labIntermediate105 min estimateArtifact: Causal split validator

Use transparent linear baselines to expose target, feature, split, scale, and slice failures before adding complexity.

Build labIntermediate110 min estimateArtifact: Linear baseline diagnostic

Understand how tree ensembles partition mistakes and when their inductive bias wins.

Build labIntermediate110 min estimateArtifact: Residual partition audit

Turn scores into actions using reliability curves and asymmetric error costs.

Systems labIntermediate100 min estimateArtifact: Calibrated threshold policy

Choose ranking losses and metrics when ordering quality matters more than labels.

Concept labIntermediate90 min estimateArtifact: Ranking evaluation contract

Design monitoring when the system changes the data it later learns from.

Systems labAdvanced110 min estimateArtifact: Drift feedback monitor