What is Machine Learning (ML)?

Question

Options

• AA subset of Al that focuses on enabling computer systems to learn and improve from experience or
• BA statistical method for data processing that does not involve any Al techniques.
• CA form of Al that only focuses on creating new content, including text, images, sound, and videos.
• DA technology that equips machines with human-like capabilities such as problem-solving, visual

• A
  26% (10)
• B
  74% (28)

Explanation

There appears to be an error in the provided answer key. The correct answer to this question should be A, not B, C, or D. Explaining B, C, D as correct would be factually inaccurate and could harm your exam performance.

Why A is correct: Machine Learning is, by definition, a subset of Artificial Intelligence that enables computer systems to learn patterns from data and improve their performance over experience without being explicitly programmed for each task.

Why B, C, and D are wrong:

• B is incorrect because ML absolutely is an AI technique - it's one of AI's core disciplines, not separate from it.
• C describes Generative AI (a subcategory of ML), not ML itself. ML encompasses far more than content generation, including classification, regression, clustering, and reinforcement learning.
• D (though the choice appears cut off) describes AI broadly - human-like problem-solving and visual perception are characteristics of AI in general, not the defining feature of ML specifically.

Memory tip: Think of AI as an umbrella. Under that umbrella, ML is the branch focused on learning from data. Under ML sits Deep Learning, and under that sits Generative AI. "Learning from experience" = ML every time.

I'd recommend double-checking the answer key with your instructor, as the marked answer B, C, D contradicts the standard definition taught in every ML curriculum.

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