Programme Outline

The Master’s Programme contents is designed around the three phases of the MLOps lifecycle. MLOps is a set of practices aimed to the development, deployment and maintenance of machine learning models.

The three phases of MLOps are each subdivided into a number of topics that cover the technical (AI/ML), ethical and practical elements of that part of the MLOps lifecycle: modelling (the exploration of the data and the design of the AI/ML models), deployment (taking AI/ML models into production and connecting to existing systems), and evaluation (maintenance of a AI/ML model in production).

This covers the core of the Human-centered AI master, common to all universities. Each university has its own order of presenting these topics, but all of them cover these materials. Moreover, each university has the opportunity to extends upon this common core; please check the individual programme pages to see the differences between the programme’s.

Focus 1: Modelling Theme.
Classic ML.

Foundations of AI

Foundations of AI

A topic that covers all of the essentials required to develop a product or deliver a service based on ethical AI technology.

AI Modelling

AI Modelling

A practical topic on AI Modelling introducing industry-based approaches, tools, methods and more.

Fundamentals of Ethics

Fundamentals of Ethics

Requirements engineering, bias detection, mitigation, and data governance. Fundamentals of ethics and information theory.

Focus 2: Implementation Theme.
Deep Learning.

Advanced AI / Deep Learning

Advanced AI / Deep Learning

A topic that provides deeper insight into the fundamental workings of AI and Deep Learning.

Organisational AI

Organisational AI

A topic, dedicated to AI in action, system architectures, data capture and collection, reinforcement learning and stream processing.

Trustworthy AI

Trustworthy AI

An introduction to Explainable AI (xAI) and practical tools to ensure transparency, reproducibility,  and interpretation of models.

Focus 3: Evaluation Theme.
Future AI.

Future AI / Learning

Future AI / Learning

This is a genuine, evidence-based thematic session, built with curiosity to inform organisational innovation.

Socially Responsible AI

Socially Responsible AI

Introduction to continuous monitoring, accountability and responsibility of artificial intelligence, and more.

Compliance & Legality

Compliance & Legality

A topic introducing legal frameworks, compliance auditing and ethical auditing, accountability and responsibility of AI.

Focus 4: Graduation.

Master Thesis Project

Master Thesis Project

 

 

 

The HCAIM programme is built based on the project-based learning (PBL) principle, in which the project (making a professional product) is placed centrally in the student’s learning trajectory.

In the Master Thesis project students show that they can independently solve challenges proposed by industry based on current needs and requirements.

 

 

 

 

 

 

 

 

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