Question

Which paradigm most accurately describes ML?

    Which paradigm most accurately describes ML?

Computer Science Illuminated

Computer Science Illuminated

Dale Nell, Lewis… 7th Edition

Chapter 9, Problem 34

Instant Answer

Solved by Expert Suzanne W.

07/07/2021

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The paradigm that most accurately describes ML (Machine Learning) is the computational and statistical paradigm. Show more…

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Which paradigm most accurately describes ML?

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Key Concepts

Machine Learning as an Empirical Paradigm

Machine learning is best characterized as an empirical, data?driven approach where models learn from examples rather than relying on explicitly programmed logic. This paradigm emphasizes learning patterns, making inferences, and generalizing from observed data to perform tasks on new, unseen inputs.

Inductive Inference

Central to machine learning is the process of inductive inference, where specific observations are used to infer general rules or models. This approach allows systems to predict outcomes by generalizing from a finite set of examples, forming the basis of many learning algorithms.

Statistical and Probabilistic Modeling

Machine learning often leverages statistical and probabilistic techniques to model uncertainty and variability in data. This framework involves using mathematical models to quantify relationships within the data and to optimize these models through techniques such as likelihood maximization or error minimization.

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