modality:tabular
Tabular
Tabular in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Linear, probabilistic, neighbor & margin models
Linear regression as a production baseline
A linear baseline is valuable because its assumptions, errors, and serving cost are easy to see.
Linear, probabilistic, neighbor & margin modelsLogistic regression, odds, and calibration
Logistic regression adds evidence in log-odds space; calibration and policy give that score operational meaning.
Linear, probabilistic, neighbor & margin modelsNaive Bayes under sparse data
Naive Bayes turns sparse counts into additive evidence—quickly, smoothly, and with assumptions in plain sight.
Linear, probabilistic, neighbor & margin modelskNN, distance, and local decisions
In kNN, representation defines the neighborhood, and the neighborhood defines the decision.
Linear, probabilistic, neighbor & margin modelsSVMs, margins, and kernels
SVMs learn from the boundary’s hardest points; kernels change geometry, not scores into probabilities.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.