Six doctoral courses — emerging digital technologies, machine learning, deep learning, generative AI, AI project design, and responsible AI — structured as a navigable knowledge graph in the Open Knowledge Format.
A narrated tour of the knowledge base — for our DBA cohort. Built entirely from member contributions.
The same knowledge base, seen from different angles.
A grounded AI tutor over the whole graph — ask in plain language and get summarized, cited answers drawn only from the knowledge base. Bring a free Gemini key, or use instant keyword search with no setup.
Ask a question → InteractiveA navigable graph of every course, session, concept and reference. Click a course to expand its sessions; open any node for its full curriculum content, with typeset math.
Open the map → BackgroundThe Open Knowledge Format — a portable, git-friendly markdown schema for knowledge bases. How this DBA corpus is modelled as concepts, courses, sessions and references.
Read the explainer → DirectionWhere this is going — the pipeline from raw course material to a published, community-maintained knowledge base, the workstreams, and the roadmap to public launch.
See the vision →The doctoral arc — from the broader digital ecosystem to the technical core, to responsible deployment.
IoT, cloud & HPC, neuromorphic computing, AR/VR/XR, RPA, digital twins, blockchain — and their strategic integration with AI.
Classical machine learning and statistical modelling — supervised & unsupervised learning, regression, clustering, time-series, model evaluation.
Neural networks and modern deep architectures, from fundamentals through training, optimisation and representation learning.
Large language models, pretraining and fine-tuning, transformers, and the generative-AI toolchain.
Designing, scoping and executing AI initiatives — from research problem identification to delivery in a business context.
Fairness, accountability, transparency, security and governance — deploying AI responsibly at organisational scale.
Every course, session, concept and reference is a markdown file with structured front-matter and cross-links — portable, diff-able, and version-controlled. The interactive map and this site are generated from it.