https://yemigabriel.github.io/UniEssexMsc/machine_learning
Introduction
During this Machine Learning (ML) module, I was provided with opportunities to learn about the legal and ethical implications of Artificial Intelligence (AI) tools while honing my technical skills through weekly practice. This multifaceted module not only deepened my understanding of various ML techniques but also challenged me to consider their real-world applications. This reflection captures my journey over 12 units, highlighting critical moments that shaped my learning experience and future goals.
When I first enrolled in this ML module, I was eager to dive deeper into the world of AI and all the complexity of the concepts involved. Despite my professional programming background, I found myself struggling with new ML concepts. Following Rolfe’s model of reflection (Rolfe et al. 2001), derived from Borton’s developmental model (Boyd et al. 1983), I will explore my experiences, challenges, and personal growth throughout this module.
What?
From the first unit, the module prioritised collaboration with my peers. We engaged in insightful discussions about the impact of system failure incidents in Industry 4.0. These discussions with my peers deepened my understanding of incidents I had previously overlooked. Being split into three teams emphasised the importance of teamwork within machine learning communities. The interactions during our first seminar further highlighted this collaborative spirit and the value of diverse perspectives in problem-solving.
However, in the following week, I encountered my first significant challenge - Exploratory Data Analysis (EDA). This was my first experience working with Jupyter Notebooks, and Python libraries such as Pandas, Numpy, and Matplotlib. Learning data preprocessing, plotting, and drawing meaningful insights from datasets was more demanding than I had anticipated. As a result, I struggled to contribute effectively to the Development Team Project, which was frustrating given my desire to support my team.
Alongside my studies, I was adjusting to life as a new father. Supporting my partner during this time meant balancing late-night responsibilities and early-morning wake-ups with my coursework. This added complexity to my learning experience, particularly in the initial weeks of the Module. I contributed little to my team project as a result.
As the module progressed, I caught up on missed readings on regression, correlation, clustering, and ANNs. Kubat (2021) and Chollet (2015) were helpful with ANNs and the Keras library. I also attended most seminars, focussing on the requirements for our Individual Project presentation. Working on my presentation took weeks of study and practice. The formative feedback I received from my tutor on my presentation proved invaluable in helping me improve my slides.
The second collaborative discussion on the benefits and risks of AI writers was my best experience in the module. I gained a deeper understanding of the topic as I worked on my posts and peer responses. I learned a lot from my peers’ contributions. I also got better at managing my referencing using PaperPile, a tool suggested by my tutor.
So What?
Reflecting on my experiences during this module, I realise the importance of collaboration and teamwork (Werth et al. 2022). Engaging in discussions about system failures in Industry 4.0 broadened my perspective on real-world scenarios. The discussion on AI writers highlighted our responsibilities as ML experts to ensure the safe, transparent, and ethical deployment of AI tools.
Working on the EDA was initially frustrating and overwhelming as I navigated Jupyter Notebooks. This frustration pushed me to develop a more resilient mindset. I explored different YouTube videos to break down the subject even further. I even used OpenAI’s ChatGPT to explain the Python code line by line. I understand that challenges are integral to growth in any learning journey. I have learned that patience and persistence are important when tackling new, complex subjects.
Balancing my role as a new father with my studies and full-time job introduced a unique set of challenges. This experience taught me the importance of adaptability and finding creative solutions to maintain balance. It forced me to prioritise effectively, ensuring I could still engage meaningfully with my studies despite the demanding nature of parenthood.
Moreover, my limited contributions to the Development Team Project prompted self-reflection on teamwork dynamics. I recognised the importance of communicating openly with my tutor and teammates about my challenges. I learned a valuable lesson: being part of a team means not only contributing your strengths but also being transparent about your limitations. This way, the group can support each other effectively.
As the module progressed and I caught up on the readings, I felt a sense of accomplishment. With renewed focus, I engaged more deeply with the subject matter. It also reinforced the significance of continuous learning and the value of seeking help when needed. I have learned that challenges are not merely obstacles but opportunities for development.
Now What?
As I continue my academic and professional journey, I plan to apply the insights and skills I gained from this module in several ways. In future projects, I will prioritise forming strong team dynamics, and actively engage with my peers to share knowledge and support one another. The challenges I faced, especially with Exploratory Data Analysis (EDA), have taught me the value of persistence and the need for a structured approach to learning. I will dedicate time to mastering new tools and concepts, ensuring that I build a solid foundation before diving into practical applications.
Additionally, my experience as a new father has equipped me with valuable time management skills. I plan to continue using structured schedules to balance my responsibilities with academic pursuits. Creating a study plan that allows for flexibility will help me make steady progress, even during busy periods. I also recognise the importance of self-care and will prioritise taking breaks to recharge, ensuring that I remain motivated.
As I continue my studies, I will remain committed to considering the ethical dimensions of my work, ensuring that I contribute to the development of safe and transparent AI tools. I intend to stay updated on emerging discussions and research in this area, as ethical considerations will be crucial in my future career.
Finally, I will leverage the skills I have developed in data analysis and machine learning as I embark on new projects. I am now more confident in my ability to analyse datasets, draw meaningful insights, and effectively communicate my findings. By applying these skills in real-world contexts, I hope to make a positive impact in the field of machine learning and contribute to innovative solutions that address modern challenges.
References
Boyd, E. and Fales, A. (1983) Reflective learning: the key to learning from experience. Journal of Humanistic Psychology, 23 (2): 99-117
Chollet, F. and Others (2015) Keras. Available at: https://keras.io.
Kubat, M. (2021). An introduction to machine learning. 3rd edn. Cham, Switzerland: Springer Nature.
Rolfe, G., Freshwater, D. and Jasper, M. (2001) Critical reflection for nursing and the helping professions. Basingstoke, England: Palgrave Macmillan.
Werth, A. et al. (2022) ‘Engagement in collaboration and teamwork using Google Colaboratory’, in 2022 Physics Education Research Conference Proceedings. 2022 Physics Education Research Conference, American Association of Physics Teachers. doi: 10.1119/perc.2022.pr.werth.