Question #5 - Reading 3 Machine Learning

Reading: Reading 3 Machine Learning

PDF File: Reading 3 Machine Learning.pdf

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Shared Context

- After Tan implements a particular new supervised machine learning algorithm, she begins to suspect that the holdout samples she is using are reducing the training set size too much. As a result, she begins to make use of K-fold cross-validation. In the K-fold cross-validation technique, after Tan shuffles the data randomly it is most likely that: A) k – 1 samples will be used as validation samples. B) k – 1 samples will be used as training samples. C) the data will be divided into k – 1 equal sub-samples.

Question

Tan is interested in applying neural networks, deep learning nets, and reinforcement learning to her investment process. Regarding these techniques, which of the following statements is most accurate?

Answer Choices:

A. Neural networks with one or more hidden layers would be considered deep learning nets (DLNs)

B. Reinforcement learning algorithms achieve maximum performance when they stay as far away from their constraints as possible

C. Neural networks work well in the presence of non-linearities and complex interactions among variables

Explanation

Neural networks have been successfully applied to a variety of investment tasks characterized by non-linearities and complex interactions among variables. Neural networks with at least three hidden layers are known as deep learning nets (DLNs). Reinforcement learning algorithms use an agent that will maximize its rewards over time, within the constraints of its environment.

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