Publication Date

5-7-2022

Document Type

Conference Proceeding

Publication Title

Soft Computing and its Engineering Applications. icSoftComp 2021

Editor

Kanubhai K. Patel, Gayatri Doctor, Atul Patel, Pawan Lingras

DOI

10.1007/978-3-031-05767-0_11

First Page

127

Last Page

139

Abstract

Machine learning is an exciting field for many, but the rigor, math, and its rapid evolution are often found to be formidable, keeping them away from studying and pursuing a career in this area. Similarity has been substantially explored in machine learning algorithms such as in the K-nearest neighbors, Kernel methods, Support Vector Machines, but not so much in human learning, particularly when it comes to teaching machine learning. In the course of teaching the subject to undergraduate, graduate, and general pool of students, the author found that relating the concepts to real-world examples greatly enhances student comprehension and makes the topics much more approachable despite the math and the methods involved. This paper relates some of the concepts, artifacts, and algorithms in machine learning such as overfitting, regularization, and Generative Adversarial Networks to the real world using illustrative examples. Most of the analogies included in the paper were well appreciated by the students in the course of the author’s teaching and acknowledged as enhancing comprehension. It is hoped that the material presented in this paper will benefit larger audiences, drawing more learners to the field, resulting in enhanced contributions to the area. The paper concludes by suggesting deep learning for automatically generating similarities and analogies as a future direction.

Keywords

Machine learning, Nearest neighbors, Learning by analogy

Comments

This version of the conference paper has been accepted for publication, 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/10.1007/978-3-031-05767-0_11

Department

Applied Data Science

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