Machine Learning simulators
Clustering, model fitting and the ideas that turn data into predictions.
k-Means
Assign points to the nearest center, move each center to its mean, repeat. Watch clusters snap into place, and sometimes into the wrong place.
DBSCAN
Density-based clustering that finds arbitrary shapes and flags outliers as noise, with no need to choose the number of clusters up front.
Hierarchical Clustering
Merge the closest groups over and over to build a dendrogram, then slice it at any height to read off clusters at that scale.
Linear Models
Regression and classification are the same straight line wx + b wearing two costumes. Change what the score turns into, and one gradient descent fits both.
Overfitting
Raise a polynomial’s degree until it threads every training point yet fails on new data, then tame it with regularization.
The Curse of Dimensionality
Add axes and space stops behaving: data thins out, a ball keeps its volume in a paper-thin crust, and near and far collapse into the same distance.
Activation Functions
Compare sigmoid, tanh, ReLU and friends alongside their derivatives, and see where flat slopes make gradients vanish.