The module map

That foundation opened a route into backpropagation and machine learning. Across the wider MSc, I explored neural networks, optimisation, statistical inference, natural language processing, clustering and model evaluation. These topics became ways to ask better questions about a service, choose an appropriate method and examine whether the result deserved confidence.

AI brings several disciplines into conversation.

Mathematics and statistics provide much of the foundation: linear algebra represents data and transformations; calculus supplies the derivatives used in backpropagation; probability helps us reason about uncertainty. Physics contributed energy-based ideas: Hopfield networks and Boltzmann machines draw on models of interacting systems and statistical physics. Neuroscience inspired aspects of artificial neural networks, while information theory supplies measures such as entropy. These are productive connections, not a claim that an artificial network reproduces a human brain.

For further reading, see Goodfellow, Bengio and Courville’s Deep Learning and the Nobel Prize’s explanation of the physics behind Hopfield and Hinton’s work.

The connection to leadership mattered most: sharing ideas, helping colleagues build confidence, and translating technical learning into analysis people could use. The Business Analysis Capability story and applied portfolio show how that developed, with each case identifying its data and delivery stage.

This is my retrospective reflection on the interview and wider course, rather than a transcript or a list of topics all discussed in the film.