The Pedagogical Challenge of Machine Learning Education

Hazzan, Orit; Mike, Koby

doi:10.1007/978-3-031-24758-3_13

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They proposed a systematization in terms of four tasks for philosophers: clarify the notion of interpretability, explain the value of interpretability, provide frameworks to think about interpretability and explore important features of it to adjust our expectations about it [22]. Another notable recent study that deals with the pedagogical aspects of machine learning was written by Hazzan and Mike [23]. They discussed challenges in the teaching of machine learning, and they provided a framework for teaching these concepts.…”

Section: Related Workmentioning

confidence: 99%

Could You Understand Me? The Relationship among Method Complexity, Preprocessing Complexity, Interpretability, and Accuracy

2023

View full text Add to dashboard Cite

The need to train experts who will be able to apply machine learning methods for knowledge discovery is increasing. Building an effective machine learning model requires understanding the principle of operation of the individual methods and their requirements in terms of data pre-preparation, and it is also important to be able to interpret the acquired knowledge. This article presents an experiment comparing the opinion of the 42 students of the course called Introduction to Machine Learning on the complexity of the method, preprocessing, and interpretability of symbolic, subsymbolic and statistical methods with the correctness of individual methods expressed on the classification task. The methodology of the implemented experiment consists of the application of various techniques in order to search for optimal models, the accuracy of which is subsequently compared with the results of a knowledge test on machine learning methods and students’ opinions on their complexity. Based on the performed non-parametric and parametric statistic tests, the null hypothesis, which claims that there is no statistically significant difference in the evaluation of individual methods in terms of their complexity/demandingness, the complexity of data preprocessing, the comprehensibility of the acquired knowledge and the correctness of the classification, is rejected.

show abstract

Section: Related Workmentioning

confidence: 99%