The compressed vocabulary from Lecture 3. Extend this file as later lectures add terms — don't redefine terms elsewhere.
| Term | Meaning |
|---|
| Task T / Experience E / Performance P | The operational definition of "learning": performance on T improves through experience E, measured by P. |
| Learning algorithm A | A fixed procedure. Reads a dataset D, returns a fitted map: A(D) = f̂_D. |
| Fitted model f̂_D | Data-dependent state produced by training. Stores what was learned; changes when D changes. |
| Training | Phase 1: examples → fit → reusable fitted map. D --A--> f̂_D |
| Deployment | Phase 2: new input becomes an action. x --f̂_D--> s --δ--> a (predict, then decide, then act). |
| Decision rule δ | Maps a score/probability s to an action a. Belongs to the deployed system, not the model. |
| Term | Meaning |
|---|
| Model family 𝓕 | The set of candidate functions {f_θ : X → S | θ ∈ Θ} considered before seeing data. Limits what can possibly be learned. |
| Fitted state | What a specific model family stores after training — e.g. coefficients (linear regression), 10 coefficient vectors (softmax regression), or the training set + a metric (kNN). |
| Parametric vs. nonparametric | Parametric models (linear/softmax regression) compress D into a fixed-size parameter vector. Nonparametric models (kNN) keep D itself as the fitted state. |
| Softmax | Converts per-class logit scores z_k(x) into a probability distribution over classes: p_k = e^{z_k} / Σ e^{z_j}. |
| Logit | A raw per-class score before softmax normalization — "evidence, not probability." |
| Argmax | Converts a probability vector into a single predicted label by picking the highest-probability class. |
| kNN (k-nearest neighbors) | Classifies a query point by majority vote among its K nearest training points, under a chosen distance metric. |
| Term | Meaning |
|---|
| MSE | Mean squared error — average squared distance between predicted and actual numeric values. Answers "how far?" Misses downstream action cost. |
| ROC / AUC | Receiver operating characteristic curve / area under it — measures how well a score ranks positives above negatives across all thresholds. Answers "how well ordered?" |
| Confusion matrix | Counts of TP / FP / FN / TN at one fixed decision threshold. Answers "which errors, and how many?" |
| Threshold t | The cutoff on a score s(x) that turns it into a binary action: δ_t(s) = 1{s ≥ t}. Changing t trades TPR against FPR — it does not change the underlying model. |
| TPR (true positive rate / sensitivity) | TP / (TP + FN) — fraction of actual positives correctly flagged. |
| FPR (false positive rate) | FP / (FP + TN) — fraction of actual negatives incorrectly flagged. |
| Group fairness gap | The same threshold t applied to the same model can produce very different FPR/FNR across subgroups G = g. "Equal rule ≠ equal burden." |
Something here unclear, or you spot an error? Ask your teaching agent — that's what it's for.