Course is designed for graduate engineers, energy professionals, operations managers, and technology leaders who want to understand both the technical foundations and the strategic dimensions of implementing artificial intelligence, especially machine learning in the offshore wind sector and related industrial environments. While the strategic focus is placed on offshore wind, the foundations are transferable across the broader energy and manufacturing landscape.
The course is structured in two complementary parts.
Part A provides an introduction to the artificial intelligence and machine learning concepts that underpin industrial AI: knowledge representation, probabilistic reasoning, gradient based learning, neural network architectures, decision trees, time series analysis, and unsupervised learning techniques.
Part B addresses the strategic, regulatory, organisational, and commercial challenges of implementing AI in practice: strategy development, business case construction, compliance, cybersecurity, intellectual property, and change management.
The learning ranges from building a working understanding of core machine learning concepts and their industrial relevance through to evaluating organisational readiness, building realistic ROI models, and navigating the regulatory landscape.
Graduates will be prepared to lead or contribute to AI strategy in offshore wind operators, energy companies, manufacturers, independent service providers, and technology consultancies serving these sectors.
- Teacher: Agata Krysiuk
- Teacher: Anna Kwiatkowska
- Teacher: Marita McPhillips
- Teacher: Piotr Szczuko