Teaching Key Machine Learning Principles Using Anti-learning Datasets

Chris Roadknight, Prapa Rattadilok, Uwe Aickelin

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Much of the teaching of machine learning focuses on iterative hill-climbing approaches and the use of local knowledge to gain information leading to local or global maxima. In this paper we advocate the teaching of alternative methods of generalising to the best possible solution, including a method called anti-learning. By using simple teaching methods, students can achieve a deeper understanding of the importance of validation on data excluded from the training process and that each problem requires its own methods to solve. We also exemplify the requirement to train a model using sufficient data by showing that different granularities of cross-validation can yield very different results.

Original languageEnglish
Title of host publicationProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
EditorsMark J.W. Lee, Sasha Nikolic, Gary K.W. Wong, Jun Shen, Montserrat Ros, Leon C. U. Lei, Neelakantam Venkatarayalu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages960-964
Number of pages5
ISBN (Electronic)9781538665220
DOIs
Publication statusPublished - 16 Jan 2019
Event2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 - Wollongong, Australia
Duration: 4 Dec 20187 Dec 2018

Publication series

NameProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018

Conference

Conference2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
Country/TerritoryAustralia
CityWollongong
Period4/12/187/12/18

Keywords

  • anti-learning
  • exclusive-or
  • hadamard
  • machine learning

ASJC Scopus subject areas

  • Education
  • Artificial Intelligence
  • Computer Networks and Communications
  • Engineering (miscellaneous)

Fingerprint

Dive into the research topics of 'Teaching Key Machine Learning Principles Using Anti-learning Datasets'. Together they form a unique fingerprint.

Cite this