Publication Date

8-11-2022

Document Type

Article

Department

Applied Data Science

Publication Title

SN Computer Science

Volume

3

DOI

10.1007/s42979-022-01339-y

Abstract

Similarity is a key element of machine learning and can make human learning much more effective as well. One of the goals of this paper is to expound on this aspect. We identify real-world concepts similar to hard-to-understand theories to enhance the learning experience and comprehension of a machine learning student. The second goal is to enhance the work in the current literature that uses similarity for transcoding. We uniquely try transcoding from Python to R and vice versa, something that was not attempted before, by identifying similarities in a latent embedding space. We list several real-world analogies to show similarities with and simplify the machine learning narrative. Next, we use Cross-Lingual Model Pretraining, Denoising Auto-encoding, and Back-translation to automatically identify similarities between the programming languages, Python and R and convert code in one to another. In the course of teaching machine learning to undergraduate, graduate, and general pool of students, the first author found that relating the concepts to real-world examples listed in this paper greatly enhanced student comprehension and made the topics much more approachable despite the math and the methods involved. When it comes to transcoding, in spite of the fact that Python and R are substantially different, we obtained reasonable success measured using various evaluation metrics and methods as described in the paper. Machines and human beings predominantly learn by way of similarity, a finding that can be explored further in both the machine and human learning domains.

Keywords

Machine learning, Similarity, Transcoding, Deep learning

Comments

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s42979-022-01339-y

Available for download on Friday, August 11, 2023

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