The Machine Learning Paradigm: From Rules to Representations

Contrasting heuristic programming with statistical learning, loss surfaces, and generalization bounds.

In traditional software engineering, developers write explicit logic and feed input data to produce outputs:

$\text{Data} + \text{Rules} \longrightarrow \text{Answers}$

In Machine Learning, we invert this paradigm:

$\text{Data} + \text{Answers} \longrightarrow \text{Rules (Learned Model)}$

The objective is not merely to memorize training answers, but to capture the underlying probability distribution so the model can generalize to unseen samples drawn from the same domain.

Machine Learning: Interactive Lab

Machine LearningMatched to lesson

Computes Euclidean distance and shifts centroids toward cluster means.

Labs:

K-Means Clustering Centroid Update Step

Python 3.13 • NumPy • PyTorch

Terminal Output

Click Run Code to execute this algorithm in the browser sandbox.

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