Machine Learning - e-Portfolio
This is the e-Portfolio for the Machine Learning module (PCOM7E April 2026) of the MSc Artificial Intelligence at the University of Essex Online.
It collects the artefacts, reflections, and evidence produced across the twelve units of the module, the team and individual projects, the two collaborative discussions, and the final 1,000-word reflective piece submitted at the end of Unit 12.
About me
I have a long career in enterprise IT, with a specialisation in cyber resiliency, security, cloud and hybrid cloud, virtualisation, and disaster recovery. I have led delivery and services organisations across Latin America, Central and Eastern Europe, the DACH region, and Central Europe at IBM, IBM-acquired Red Hat, and VMware (now Broadcom). The Machine Learning module sits in the context of the MSc Artificial Intelligence at the University of Essex Online, a conversion programme aimed at experienced practitioners moving formally into AI. It is the fourth assessed module of the programme for me.
A fuller account of background and self-directed foundations relevant to this module is on the Professional Context page.
Module overview
The Machine Learning module runs from late April to mid-July 2026 across twelve units. It has three summative components: a Unit 6 team project report and peer review (8 June 2026), a Unit 11 individual presentation (13 July 2026), and the Unit 12 e-Portfolio submission with embedded 1,000-word reflective piece (20 July 2026). The four module learning outcomes are stated below; the rest of the site is structured around them.
How to read this site
The top navigation links to the eight main sections:
- Units - one page per module unit (1 to 12), with what was covered, what I did, the artefacts produced, in-unit reflection, and references.
- Projects - hub for the Unit 6 team project, the Unit 11 individual project, and the explicit Unit 11 vs Unit 6 evaluation.
- Discussions - the two assessed Collaborative Discussion forum activities (CD1 across Units 1 to 3, CD2 across Units 8 to 10).
- Reflection - the 1,000-word reflective piece for the Unit 12 submission.
- Professional Development - Professional Skills Matrix, Personal Development Plan, and feedback received during the module.
- Evidence Index - catalogue of every evidence item on the site with its full Harvard reference and direct link.
- References - consolidated Harvard Cite Them Right bibliography for the whole site.
The site is built with Jekyll and the Minima theme, hosted on GitHub Pages. Source: github.com/protode908/eportfolio-uoe.
Module learning outcomes
This e-Portfolio addresses the four learning outcomes of the module:
LO1. Articulate the legal, social, ethical, and professional issues faced by machine learning professionals.
Where evidenced: Collaborative Discussion 1 (CD1), Collaborative Discussion 2 (CD2), Team Project (Unit 6), Individual Project (Unit 11), and unit reflections across the module.
LO2. Understand the applicability and challenges associated with different datasets for the use of machine learning algorithms.
Where evidenced: unit pages covering dataset work (Units 3, 4, 5, 6, 8, 9, 10, 11), Team Project (Unit 6), Individual Project (Unit 11), CD1, CD2.
LO3. Apply and critically appraise machine learning techniques to real-world problems, particularly where technical risk and uncertainty is involved.
Where evidenced: unit pages covering modelling activities (Units 5, 7, 8, 9, 10, 11), Team Project (Unit 6), Individual Project (Unit 11), Project Comparison.
LO4. Systematically develop and implement the skills required to be an effective member of a development team in a virtual professional environment, adopting real-life perspectives on team roles and organisation.
Where evidenced: Team Project (Unit 6) (the central evidence base), Project Comparison, Professional Development, and the Unit 2 team-formation work.
The full per-artefact mapping appears on each unit and project page. The Evidence Index catalogues every individual evidence item with its citation reference. A by-LO mapping table (which evidence ticks which learning outcome) is on the Learning Outcomes Mapping page.