This page is the by-LO catalogue: each module learning outcome with the artefacts that evidence it, in tabular form. It complements the Home page (which has the same synthesis at a higher level) and the Evidence Index (which is the by-evidence catalogue with full Harvard references).

This page is reachable from Home but not in the top navigation. It exists for tutors and readers who want a single LO-by-LO view of every artefact across the e-Portfolio.

The four module learning outcomes

The Machine Learning module has four learning outcomes (University of Essex Online, n.d.):

LO1. Articulate the legal, social, ethical, and professional issues faced by machine learning professionals.

LO2. Understand the applicability and challenges associated with different datasets for the use of machine learning algorithms.

LO3. Apply and critically appraise machine learning techniques to real-world problems, particularly where technical risk and uncertainty is involved.

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.

LO mapping table

The table below maps every catalogued evidence item to the learning outcomes it evidences. Items are added incrementally as the module progresses. Each row links to the underlying evidence on the relevant page; the Evidence Index entry has the full Harvard reference for citation in the reflection.

Evidence Where it lives LO1 LO2 LO3 LO4
E2.1 Kick-off deck slide 1 - title Unit 2       x
E2.2 Kick-off deck slide 2 - team intro template Unit 2       x
E2.3 Kick-off deck slide 3 - working agreement Unit 2 x     x
E2.4 Kick-off deck slide 4 - comms and tooling (1 of 2) Unit 2       x
E2.5 Kick-off deck slide 5 - comms and tooling (2 of 2) Unit 2     x x
E2.6 Kick-off deck slide 6 - submission scope Unit 2     x x
E6.1 Track 1 decision register (D-001 to D-018) Team Project     x x
E6.2 Track 1 design document Team Project   x   x
E6.3 Briefing pack to colleague (data-cleaning lead) Team Project       x
E6.4 Three-tier justification framework Team Project     x  
E6.5 Triple-source evaluation map Team Project     x  
E6.6 CRISP-DM five-script Python pipeline Team Project   x x  
E6.7 Demand-mapping pivot (D-017) Team Project     x  
E6.8 Will-NOT-claim list (Section 5 of the report) Team Project x   x  
E6.9 Committed ML design constraints upfront (D-011) Team Project   x x  
E6.10 Pre-run design document for EDA and cleaning Team Project   x   x
E6.11 Cleaning script 02_data_preparation.py Team Project   x x x
E6.12 Group D Google Drive workspace Team Project       x
E6.13 Group D project plan and timeline Team Project       x
E6.14 9 May 2026 meeting notes and summary email Team Project       x
E6.15 Decision register entry D-019 Team Project     x x
E6.16 Business question slide Team Project   x x  
E6.17 9 May 2026 group meeting presentation (12 slides) Team Project x x x x
E6.18 10 May 2026 EDA pre-run handover email (redacted) Email of record     x x
ECD1.1 CD1 initial post (automotive: BMW and Jaguar Land Rover) CD1 x x    
ECD1.2 CD1 peer response on HSE Ireland ransomware and healthcare resilience CD1 x      
ECD1.3 CD1 peer response on pharmaceutical Industry 4.0 and Zero Trust CD1 x      
ECD1.4 CD1 summary post: cross-sector implementation-gap synthesis CD1 x x    
(further rows added as evidence is catalogued)          

Reading the table

A tick (x) in an LO column indicates the evidence item directly evidences that learning outcome. The detailed “Evidences LOx because…” mapping is on each evidence item’s host page (the unit, project, or discussion page where it is anchored).

The reflection on the Reflection page draws on this evidence base when synthesising across the four learning outcomes; in-text citations there resolve to the References page.

References

University of Essex Online (n.d.) Machine Learning module description. Available at: https://www.online.essex.ac.uk/ (Accessed: 10 July 2026).