Term tag: Machine Learning

A/B Testing

A/B testing is the randomized experiment that decides whether a model, prompt, or feature change truly improves a live metric — or just moved on noise.

Active Learning

Active learning lets a model choose which unlabelled examples to get labelled next, hitting target accuracy with far fewer labels than random labelling.

AI Drug Discovery

What AI actually does at each stage of drug development, why it speeds up the cheap part, and where AI-designed molecules really stand in clinical trials.

AI in Finance

Machine learning that approves loans and blocks card fraud — where you ship the most accurate model you can defend, not the best one you can build.

AI in Science

Three different things get called AI for science. Two have produced verified results, the third mostly has not — and what got revised down.

Anomaly Detection (AD)

A 99%-accurate detector can still hand you a queue that is 99% false alarms. How anomaly detection scores, thresholds and ranks rare events.

Artificial Intelligence (AI)

AI is software whose behaviour is fitted from data instead of written rule by rule: what that means, why the label keeps moving, and where you already use it.

Bias in AI (Algorithmic Bias)

Why AI systems produce unfair outcomes: how bias enters through data and proxy labels, and why no model can satisfy every definition of fairness at once.

Bias-Variance Tradeoff

Statistical bias, not fairness. Error splits into bias squared, variance and irreducible noise — derived, worked on numbers, and honest about where it breaks.

Calibration

Calibration means a model's confidence matches reality: of everything it calls 80% likely, about 80% should happen — a property separate from raw accuracy.

Catastrophic Forgetting

Why fine-tuning makes a model worse at everything it used to do well — the mechanism inside shared weights, and what each fix actually costs.

Causal Reasoning

Why a model trained on observational data cannot tell cause from correlation, what that costs when you act on it, and what actually identifies one.

Class Imbalance

Why a fraud model can score 99% accuracy and catch nothing — the imbalance trap, why precision/recall/PR-AUC replace accuracy, and how to fix it.

Classification (CLF)

Accuracy is usually the wrong metric. What a confusion matrix, precision, recall and the decision threshold really tell you about a classifier.

Clustering

Grouping unlabeled data into clusters: which algorithm to pick, why each one imposes a shape, how to choose k, and how to tell if the clusters are real.

Concept Drift

Concept drift is when the relationship a deployed model learned changes over time, so accuracy silently decays until you detect the drift and retrain.

Conformal Prediction

A model-agnostic, distribution-free method that turns any prediction into a set guaranteed to contain the true answer at a chosen rate, such as 90%.

Continuous Learning (CL)

Keeping a deployed model current as the world moves — why teams schedule batch retraining instead of true online learning, and what breaks if they don't.

Cross-Validation (CV)

How cross-validation turns one lucky train/test split into a stable estimate, why 5 or 10 folds, and the leakage that quietly inflates every score.

Data Analysis

What the work consists of: framing a question, looking before aggregating, choosing a summary that does not lie, and turning a number into a decision.

Data Augmentation

Making new training examples by transforming existing ones. Valid only when the label is indifferent to the transform — and it adds variety, not information.

Data Leakage

Data leakage is training a model on information it will not have at prediction time. It pushes scores up, not down, which is why review never catches it.

Decision Trees (DT)

How a decision tree actually chooses each split: Gini impurity worked by hand, why depth drives overfitting, and what a single tree cannot represent.

Deep Learning

What makes deep learning deep: each layer builds on the one below, so capacity grows multiplicatively with depth and only additively with width.

Dimensionality Reduction (DR)

Why high-dimensional data has to be compressed, how PCA picks its axes, and why cluster sizes and gaps in a t-SNE or UMAP plot mean nothing.

Early Stopping

Early stopping halts training once validation loss stops improving, so the model generalises instead of memorising. How patience and best-weight restore work.

Educational AI

AI that tutors, adapts practice and grades work — and what forty years of effect-size research actually shows about whether it improves learning.

Embedding

What an embedding vector actually is, why cosine similarity beats Euclidean distance, what dimensions buy you, and where the geometry misleads you.

Ensemble Methods

Why averaging models reduces error and where it stops: the variance of a mean of m correlated predictors, the correlation floor, and what bagging cannot fix.

Feature Scaling

Feature scaling puts numeric features on comparable ranges so no variable dominates by its units alone — why distance- and gradient-based models need it.

Feature Selection (FS)

Which columns to keep and how to decide: filter vs wrapper vs embedded, why 2^n subsets rules out brute force, and the CV mistake that fakes accuracy.

Federated Learning

Federated learning trains a shared model across many devices without moving their raw data: the local-train, send-updates, aggregate loop.

Fine-tuning (FT)

What fine-tuning changes inside a model, what it costs in GPU memory, and why it reliably buys format and style but not new facts.

Generalization

Why a model that scores 99% in training fails in the world: the generalization gap, the test-set size needed to measure it, and why classical theory is wrong.

Gradient Boosting

Each tree is fitted to the negative gradient of the loss, not to the residuals — a boosting round worked by hand, and what changes under log loss.

Gradient Descent

The rule that turns a gradient into a weight update. Worked on f(x)=x²: why the step size alone decides between converging, oscillating and blowing up.

High Bias

Statistical bias, not AI fairness: the error a model class cannot escape even with infinite data — worked in closed form on a line fitted to a parabola.

Hyperparameter

A setting you choose before training that gradient descent never updates — learning rate, batch size, layer count — and why a bad one caps the model.

Inference

Running a trained model to get an answer. Training is paid once; inference is paid on every request — and it is where most AI money goes.

Information Gain

Entropy worked from scratch, one split computed by hand in bits, and why a unique ID column scores the maximum possible information gain.

k-Nearest Neighbors (kNN)

How k-Nearest Neighbors classifies a point by majority vote of its k closest neighbours: choosing k, the distance metric, and the curse of dimensionality.

Logistic Regression (LR)

Logistic regression turns weighted evidence into a probability with the sigmoid: why the name says regression, how to read coefficients, and when to use it.

Loss Function

The number a model is trained to make smaller. Its slope, not its value, moves every weight — so the loss you pick decides what the model learns.

Low Variance

Low variance means values sit close to their average — and for a model, that retraining it on fresh data barely moves its predictions. Worked on numbers.

Low-Rank Adaptation (LoRA)

LoRA freezes a model's weights and trains two thin matrices beside each one: 98,304 numbers instead of 151 million, and no added inference latency.

Machine Learning (ML)

How a machine finds a rule nobody wrote: a line fitted to four points by hand, loss falling from 433.5 to 6.38 in three steps, and why 98% accuracy lies.

Machine Learning Operations (MLOps)

Why shipping a model is not like shipping software: the artifact has three inputs — code, data and trained weights — and only one of them lives in git.

Machine Unlearning

Removing the influence of specific training data from an already-trained model without full retraining, driven by erasure law, copyright, and safety.

Membership Inference

A privacy attack that decides whether a specific record was in a model's training set, exploiting that models are more confident on data they memorized.

Monitoring

Why a deployed model rots while every dashboard stays green — and what you can actually measure when the true labels arrive weeks late, or never.

Naive Bayes

A probabilistic classifier applying Bayes' theorem under a deliberately false feature-independence assumption — fast, data-light, a strong text baseline.

Overfitting

Overfitting is when a model memorizes its training data, including the noise, so it scores high on training data but fails on new, unseen data.

Pattern Recognition (PR)

Pattern recognition is the automatic discovery and classification of regularities in data — the older name for what became statistical machine learning.

Pre-trained Models

A neural network someone else already trained on a huge general dataset, which you download and adapt to your task instead of training from scratch.

Precision and Recall

Precision, recall, F1 and the confusion matrix worked through one imbalanced example — and why the model with the best accuracy can catch nothing at all.

Precision Medicine

How precision medicine tailors care to a person's genome and clinical data, and what AI does in it: variant interpretation, risk, and drug response.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) reduces dimensions by projecting data onto orthogonal directions of maximum variance, ranked by the variance each keeps.

Privacy

In AI, privacy is about what a model can memorize and leak about people, and the techniques—differential privacy, federated learning—that limit it.

Quantum Computing (QC)

Quantum computing uses qubits — held in superposition and entangled — for exponential speedups on a narrow set of problems, not general computing.

Random Forest (RF)

A random forest averages many decision trees, each grown on a bootstrap sample and split on a random subset of features, to cut variance without adding bias.

Recommendation Systems

A recommendation system predicts what a user will want — a film, product, or song — from patterns in past behavior, and surfaces it automatically.

Regression

The supervised-learning task of predicting a continuous number — a price, a temperature, a duration — from input features, in contrast to classification.

Regularization

Regularization deliberately constrains a model so it fits training data less perfectly, trading a little training accuracy for much better generalization.

Scaling Laws

The empirical curves that make AI capex rational: model loss falls predictably with compute, data and parameters — and what they do not promise.

Supervised Fine-Tuning (SFT)

Supervised fine-tuning trains a pretrained language model on curated prompt-response pairs with next-token loss, teaching it to follow instructions.

Supervised Learning

Training a model on labeled input-output pairs so it learns a mapping it can apply to new, unlabeled inputs.

Support Vector Machines (SVM)

A classifier that separates two classes with the widest possible margin, using support vectors and the kernel trick to handle non-linear data.

Synthetic Data

Data generated by a model instead of collected from the world: what synthetic data is, where it helps, and why recursive use triggers model collapse.

Tensor Operations

The array manipulations — elementwise math, matrix multiplication, reshaping, broadcasting, reduction — that every neural network is built out of.

Time Series

Data points indexed in time order, where the sequence matters and rows aren't independent — analyzed to forecast future values or flag anomalies.

Training

Training fits a model's parameters to data: forward pass, measure the loss, backward pass, nudge every weight downhill, repeat until the loss stops falling.

Transfer Learning (TL)

Transfer learning reuses a model trained on one large task as the starting point for a related task, so you need far less labeled data and compute.

Underfitting

Underfitting is when a model is too simple to capture the real pattern, so it does poorly on both training and new data — the mirror of overfitting.

Unsupervised Learning

Finding structure in unlabeled data — no correct answers are given, so the model discovers groupings, patterns, or a compressed representation on its own.