A company wants to build an ML model to detect abnormal patterns in sensor data. The company does not have labeled data for training. Which ML method will meet these requirements?

The correct answer is D. Autoencoders. Autoencoders are a type of unsupervised learning method used to detect abnormal patterns (anomalies) in sensor data when labeled data is not available. They learn to reconstruct normal patterns, making deviations (anomalies) easily detectable.

Submitted by marco_it· Mar 30, 2026Fundamentals of AI and ML

Question

Options

ALinear regression
BClassification
CDecision tree
DAutoencoders

How the community answered

(19 responses)

A
5% (1)
B
5% (1)
C
16% (3)
D
74% (14)

Explanation

Autoencoders are a type of unsupervised learning method used to detect abnormal patterns (anomalies) in sensor data when labeled data is not available. They learn to reconstruct normal patterns, making deviations (anomalies) easily detectable.

Topics

#autoencoders#anomaly detection#unsupervised learning#unlabeled data

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