What is true regarding an Autoencoder (AE)?

I It is a neural network with a specific structure.
II It is an unsupervised learning algorithm.
III It is an algorithm to train a regression tree.
IV It is a supervised learning algorithm.

Question

What is true regarding an Autoencoder (AE)?

I	It is a neural network with a specific structure.
II	It is an unsupervised learning algorithm.
III	It is an algorithm to train a regression tree.
IV	It is a supervised learning algorithm.

BlackTom AI · Accepted Answer

Let's restate the question and options to frame our analysis clearly.
Question: What is true regarding an Autoencoder (AE)?
Options:
I. It is a neural network with a specific structure.
II. It is an unsupervised learning algorithm.
III. It is an algorithm to train a regression tree.
IV. It is a supervised learning algorithm.

Now, evaluate each option one by one.

Option I: It is a neural network with a specific structure.
This statement is generally true. An autoencoder is a neural network designed with a particular architecture that includes an encoder part compressing the input to a latent representation and a decoder part reconstructing the input from that representation. The structure is intentional (bottleneck, symmetric or otherwise) to learn meaningful encodings. The emphasis is on the architectural design that enables the reconstruction task, which aligns with the standard definition of an AE.

Option II: It is an unsupervised learning algorithm.
This statement is also true. Autoencoders are typically trained in an unsupervised manner: they use input data as both the input and the target for reconstruction, so there are no external labels required. The learning signal comes from comparing the input to its reconstruction, not from labeled supervision.

Option III: It is an algorithm to train a regression tree.
This statement is false. Training a regression tree is a decision-tree-based supervised learning method (e.g., CART), which is conceptually different from an autoencoder. An AE learns representations through neural networks and backpropagation, not by constructing decision trees for regression.

Option IV: It is a supervised learning algorithm.
This statement is generally incorrect in the standard framing. While autoencoders can be used as components within supervised pipelines or for semi-supervised learning, the core autoencoder training objective (reconstructing the input) is not supervised by external labels. Therefore, labeling the classic AE as supervised would be misleading.

Putting these assessments together, the true statements are I and II, while III and IV are incorrect due to the distinctions between neural network architecture for encoding/decoding, unsupervised learning signals, and the nature of regression-tree training and supervision.

In summary, I and II reflect the true nature of Autoencoders, whereas III and IV describe concepts that do not align with the standard definition or purpose of autoencoders.

FINTECH 540.01.Fa25 Final Exam

What is true regarding an Autoencoder (AE)? I It is a neural network with a specific structure. II It is an unsupervised learning algorithm. III It is an algorithm to train a regression tree. IV It is a supervised learning algorithm.

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