Machine Learning

Machine Learning - Unit 1: Introduction to Machine Learning

Overview

Machine Learning (ML) has become an integral part of our daily life. From browsing on internet through to getting a bank loan, ML is used to make decisions and for profiling. The availability of big data on a real-time basis facilitates the learning of the ML algorithms, which in turn provides machines with the ability to make more accurate decisions autonomously

My Reflection

In the first week of the module, we started with an overall introduction to machine learning through the lecturecast, as well as the readings, which included a variety of topics, ranging from machine learning concepts to application scandals, as well as the intersection of machine learning with Industry 5.0. The latter point would be essential to start off the first collaborative discussion in the same week.

We also had our first seminar, in which the module's group assignment and allocation was announced. Right after the seminar, I started reaching out to my group colleagues to establish our communication.

Artefacts: Collaborative Discussion 1: The 4th Industrial Revolution

Initial Post

In the first unit, we started an initial discussion about the impact of industry 4.0 and/or Industry 5.0 on the sector in which we are involved or interested.

My initial post:

The information system that I pose in this discussion is attendance and task management systems, as they were the constant struggle of the organisation that I worked for over the last four years. The organisation continuously struggled with how to monitor and manage the attendance and engagement of employees in a remote setting. The organisation’s attempts to tweak its systems usually resulted in deeper discontentment and deleterious behaviour.

There is a considerable body of research on attendance and employee monitoring systems. Every now and then, a new research paper introduces some enhancements in the systems to be more efficient and more accurate (Hartono et al., 2024). The great majority of the research focuses on the technology’s efficiency and accuracy (Ali et al., 2022), which is clearly driven by the zeitgeist of Industry 4.0 (Schwab, 2016) that now can be perceived as technology-centric, or at best business-centric.

On the other hand, Industry 5.0 aims to shift the centricity to revolve around humans, as human-centricity is one of the framework’s three main pillars (Nousala, Metcalf and Ing, 2024). Under this approach, the focus in such discussions should revolve around human beings working under those monitoring systems. Instead of focusing on the accuracy of the monitoring, the effects of it on human mental and physical health, in addition to productivity among others, should be the priority.

Reference List

Ali, N.S. et al. (2022) ‘Automated Attendance Management systems: Systematic Literature Review’, International Journal of Technology Enhanced Learning, 14(1), p. 37. Available at: https://doi.org/10.1504/ijtel.2022.120559.
Hartono, T. et al. (2024) ‘Importance Increasing Attendance Efficiency Accuracy with Presence System in Era Industrial Revolution 4.0’, International Journal of Cyber and IT Service Management, 4(2), pp. 133–142. Available at: https://doi.org/10.34306/ijcitsm.v4i2.168.
Nousala, S., Metcalf, G. and Ing, D. (eds) (2024) Industry 4.0 to Industry 5.0, Translational Systems Sciences. Singapore: Springer Nature Singapore. Available at: https://doi.org/10.1007/978-981-99-9730-5.
Schwab, K. (2016) The Fourth Industrial Revolution: What It Means and How to Respond, World Economic Forum. Available at: https://www.weforum.org/stories/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/ (Accessed: 1 August 2025).