AdaBoost
Decision stumps trained one after another on weighted data: mistakes grow, the next stump targets them, vote weight α per round, the ensemble's exact staircase boundary, train and test error over up to 60 rounds.
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Grasp the core algorithms of machine learning interactively.
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Anatomy of a prompt
Combine role, task, context, format and examples as stackable 3D blocks. Toggle components to improve a sample prompt without making AI calls.
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Data privacy traffic lights
Explore which administrative information belongs in a prompt, needs anonymisation or an approved tool, or must stay out of public AI. Includes explanations and a self-test; no AI calls.
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Decision Tree
Axis-parallel splits by information gain: ΔI for every candidate threshold, tree diagram linked to the plane, depth, minimum leaf size and pruning, train against test accuracy over the depth.
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Ensemble: Bagging vs. Boosting
Many shallow decision trees combined into one strong model: single tree (jagged staircase), bagging (average out variance → smooth boundary) and boosting (sequentially on hard cases). Interlocking "two moons", accuracy over the number of trees — Random Forest made tangible.
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k-Means
Lloyd's algorithm as a chain of states without labels: assign and move centres, Voronoi cells, inertia per half-step, random vs. k-means++, local minima, elbow with silhouette and typical failure cases.
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k-Nearest Neighbors (kNN)
A query point, its k neighbours with distance shape and majority vote: decision regions from k = 1 (jagged) to k = 40 (smooth), train and test accuracy over k, metric and feature scaling change the neighbourhood.
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AI theatre
Explore how a neural network recognises digits and learns through four linked 3D scenes. The AI tutor selects a route through neurons, recognition, training and backpropagation.
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AI timeline
Explore seventy years of AI history on an interactive 3D timeline, from Turing and the AI winters to ChatGPT, with selectable milestones and event details.
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Classifier arena
Eight classifiers learn on the same data: kNN, logistic regression, linear and RBF SVM, naive Bayes, decision tree, random forest and a neural network — their decision boundaries reveal the model family, train and test accuracy side by side expose overfitting.
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Machine learning paradigms
Try supervised learning, unsupervised clustering and reinforcement learning in an animated 3D scene. Separate classes, find groups and collect rewards.
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Linear separating plane (3D)
A linear classifier in space: two point classes, one separating plane with normal vector w. Rotate/shift the plane with sliders or auto-separate via the perceptron — separable, overlapping and XOR data reveal the strength and the limit of the linear model.
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Metrics Lab: confusion matrix & metrics
Slide a classifier's threshold and watch cases move between TP, FP, FN and TN — accuracy, precision, recall, specificity and F1 with their formulas, the precision–recall curve and the accuracy paradox with rare positives.
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AI model landscape 2026
Compare major AI model families by capability, reasoning depth and openness in a 3D bubble chart. Bubble size represents context length; a qualitative snapshot without version numbers.
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Model validation: train, validation, test
Split, validation curve, k-fold cross-validation and a one-time test on a polynomial regression — plus data leakage and tuning on the test set as counterexamples.
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Naïve Bayes classifier
Prior times per-feature likelihoods: posterior field with marginal densities, axis-aligned ellipses and a quadratic boundary; correlated data show what the independence assumption misses; plus a spam mode with log-odds.
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PageRank
Build your own link graph, view the transition matrix live, and let the random surfer wander through the graph step by step (also backward or tenfold) — the PageRank values visibly converge, with interpretation.
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PCA — Principal Component Analysis
Principal axes of a 3D point cloud as eigenvectors of the covariance: projection onto k components with perpendiculars, reconstruction error = sum of the discarded λ, scree plot, standardising and the difference from the regression plane.
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Random Forest
Many random decision trees vote: the majority smooths the jagged boundaries of single trees and lowers the test error — bootstrap samples, feature randomness, out-of-bag error and test error over the number of trees.
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ROC curve & AUC
Two score distributions, the threshold sweeps: the ROC curve is traced point by point, AUC as the share of correctly ranked pairs, ROC stays the same at any prevalence, two models with crossing curves compared.
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Support Vector Machine (SVM)
Maximum margin and support vectors, solved exactly with SMO: points without a ring do not move the boundary, soft margin C, slack ξ, linear, lift (paraboloid in 3D) and RBF kernels.
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