Algorithm

Algorithms and Models in Machine Learning

In machine learning, the algorithm is the learning procedure and the model is what it learns. Gradient descent is an algorithm; the trained network with its millions of adjusted weights is a model. Run the same algorithm on different data and you get a different model.

The distinction has practical weight. When a regulator asks to audit “the algorithm” behind a lending decision, what actually needs inspecting is the trained model and the data it learned from; the textbook training procedure on its own reveals almost nothing about how individual applicants are treated.

“The Algorithm” in Everyday Speech

When people say a platform’s algorithm chose their feed, they mean something broader: the whole recommender system, including trained models, ranking rules, and business logic layered on top. No single recipe of steps decides what you see.

That everyday sense is loose but points at something real. A pipeline of automated decisions shapes what millions of people read and watch, which is why “the algorithm” became shorthand for automated influence itself.

Example of an Algorithm

Gradient descent, the workhorse of model training, shows the recipe-versus-result distinction in action. It starts a model with random weights and measures how wrong the model’s predictions are on the training data.

Then it loops: nudge each weight in the direction that reduces the error, measure again, repeat, thousands or millions of times. The steps never change; they are the algorithm.

What comes out the other end is the trained model. The recipe is public knowledge printed in every textbook; the model it baked, and the data it baked it from, are where the value and the risk live.

Related AI terms: Model · Machine Learning · Heuristic · Gradient Descent · Artificial Intelligence

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