Modelling (Module A)
The first module, namely Modelling (Module A), focuses on the first phase of the MLOps lifecycle and is related to the lowest maturity level of the application of Machine Learning (ML) in organizations: modelling data. It includes the activities that form the basis of the application of ML, such as data extraction, data analysis, data preparation, model training and (mainly manual) model validation and evaluation.
In this phase, the focus is on correctly analyzing and modelling the data to achieve the business objectives and little use is made of automation (e.g. CI/CD), which is only added in the second phase of MLOps (Deployment – Module B). The modelling activities are often characterized by the manual, script-driven and interactive method by which the data analysis, preparation, model training and validation are carried out. To maintain an overview of the different models, parameters and choices that are being experimented with, experiment tracking is used.
From an ethical perspective, it is important in the modelling phase to devote sufficient time and attention to finding out the client’s objectives, mapping the stakeholders and exploring how the individual values of these stakeholders are affected (and recognizing possible conflicts between them). Aspects such as transparency, inclusion, security and privacy are of great importance in this. Naturally, attention must also be paid to the social and moral desirability of the client’s objectives. In addition, it is important to have (timely) awareness of possible biases/prejudices in the available data, recognise the possible consequences of these prejudices and find mitigations to deal with these prejudices.
Learning Outcomes
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The student evaluates various ML techniques to make a well-founded choice, matching the required requirements of the custormer and implementing a prototype of the chosen ML technique to advise on solving a given data modelling problem
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The student argues, using fundamental ethical frameworks how moral dilemmas can be solved and evaluates the possible consequences of existing biases in data and the influence of designed mitigations to counteract the consequences of this biases.
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The student applies quantative and qualitative research methods to scientifically substantiate their choices during the ethical consideration(s) and making of the prototype.
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| LEARNING OUTCOME 1 |
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LEARNING OUTCOME 2 |
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LEARNING OUTCOME 3 |
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
 |
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Technical focus: Foundation of AI
General AI
Data Exploration for Machine Learning
Machine Learning Fundamentals
Decision Theory
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Practical focus: AI modelling
Data Science
Supervised Machine Learning
Unsupervised Machine Learning
ML applications
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Ethical focus: Ethics fundamentals
General Ethics
Ethical Frameworks
Advanced Ethics
Applied Ethics
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Deployment (Module B)
The module Deployment (Module B) focuses on the second phase of the MLOps development cycle; the deployment. After the data exploratory phase of modelling (see Module A – Modelling), comes the integration of the ML solution into the business systems. It is now important to start thinking about the ML architecture and how it plays together with the existing systems (legacy). To experience real benefit from automated ML solutions, pipelines need to be introduced; on the one hand, to be able to deal with continuous and live data supplies (stream processing), and on the other hand, to link the results of the ML model to other systems.
Moreover, Module B enhances the complexity of AI technology by moving towards (the use of) neural networks and deep learning. A major advantage of these more complex models is that they are more flexible and versatile than the techniques introduce in Module A – Modelling. However, the important disadvantages of these techniques are that they are more complex (to understand and configure) and opaquer. Therein lies an important ethical dilemma in the use of (advanced) AI techniques: how do you still understand what the AI solution calculates and whether this is done in the right way. Making the deployment of AI solutions more transparent and being able to determine the possible risks and mitigate these risks are important (social) themes in this module.
Learning Outcomes
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The student assesses the possible choices for the integration of an advanced AI technique, such as Deep and/or Reinforcement Learning, and authors an one-page report, based on a prototype that has been developed taking into account the limitations of and influences on the existing ICT systems and data facilities of the customer, which have been obtained in collaboration with, for example, ICT architects or developers.
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The student assesses the potential risk involved and tests the degree of transparency (includien interpretability, reproducibility and explainibility) of a chosen AI/ML implementation and designs solutions using techniques that increase insight and transparency among Stakeholders (so called Explainable AI (XAI) techniques) to remedy shortcomings in this respect compared to the social and customer specific requirements.
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The student formulates a research design for a scientifically sound (practic oriented) research project related to a company case by formulating a relevant, consistent, functional research question, considering the applied research methods to be used, and establishing a precise, relevant and critical theoretical framework.
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| LEARNING OUTCOME 1 |
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LEARNING OUTCOME 2 |
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LEARNING OUTCOME 3 |
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Technical focus: Deep Learning
Fundamentals of Deep Learning
Optimization of Deep Learning
Applications of Deep Learning
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Practical focus: Organisational AI
MLOps
Deployment of AI
Quality of Development & Deployment
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Ethical focus: Trustworthy AI
General Explainable AI
Privacy
Security and robustness
Risk
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Evaluation (Module C)
The Evaluation module (Module C) focuses on the evaluation aspects of AI development including both the societal aspects of an AI product, and the development of an appreciation of the potential future directions that AI may take, looking at technology trends; socially responsible AI; compliance, as well as ensuring that the human element is ever-present in the design, development, and evaluation of AI systems.
As part of the future of AI, an exploration of the level of AI adoption in different industries is discussed, as well as how AI is adapted for different domains. Looking at socially responsible AI includes how AI affects individuals and different groups in society. And as a crucial part of the module, there is a focus on laws, policies and codes of conduct related to AI (with an emphasis on issues such as explainability and trust), as well as quality control and quality management processes, to evaluate the results of AI initiatives.
Learning Outcomes
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The student develops an appreciation of the cutting-edge approches of AI and machine learning, as well as an understanding of how artificiale intelligence is utilized in different domains, and how to evaluate the potential directions artificial intelligence may go in the future.
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The student shows a well-defined approach to consequence scanning, considering issues such as evaluating the potential impact new technology could have on individuals and society, focussing specificaally on minorities and marginilized groups as well as potential environmental impact..
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The student demonstrates the ability to emply a full-articulated research methodolgy with ethics embedded at all stages, with an awareness of the contextual nature of the specific approaches that should be utilized which will be informed by the case studies covered in this module.
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| LEARNING OUTCOME 1 |
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LEARNING OUTCOME 2 |
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LEARNING OUTCOME 3 |
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Technical focus: Future AI
Introduction
Open Problems and Challenges
Advances in ML Models Through an HC Lens. A Result-Oriented Study
Emerging Evaluations for HCAI Models – Discussion-Based Study
Philosophical Discussion on Future AI technology
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Practical focus: Socially responsible AI
Scope Of Socially Responsible AI
Corporate Social Responsibility (ISO 26000) – When Using HCAI System
Socio-Legal Aspects For AI
AI For All
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Ethical focus: Compliance, Legality, Humanity
EU And International Legislation/Frameworks On Data, AI, Human Rights And Equality
Data Management, Audit And Assessment
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Graduation (Module D)
The Graduation module (Module D) reflects the core principle of the HCAIM programme that is built on the concept of project-based learning (PBL). The goal of this module is to position the graduation project (making a professional product) centrally in the student’s learning trajectory. As part of their Graduation project (the Master Thesis), students show that they can independently solve challenges proposed by the industry based on current needs and requirements, considering both the technical and the ethical aspects of the issue at hand.
Each thesis is considered locally, with an internal supervisor (a professor from the University in which the student is pursuing the degree) and an external supervisor belonging to the party proposing the thesis (if any). This latter aspect, despite not being mandatory, is rigorously pursued. The proposing party can be an SME, an Excellence Centre, or another University, both at a national and international level. Proposing parties are expected to provide both national and international thesis (i.e. thesis organised in with a University from the same country or from a foreign one).
Learning Outcomes
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The student recognizes and reflects on the AI lifecycle in a realistic, industry-informed context, and in diverse locations, scenarios and use cases.
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The student demonstrates a robust and valid research attitude through a project with a well-defined interdisciplinary approach producing industry-relevant and technologically competent solutions, while evaluating the potential impact of their work on individuals and society.
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The student demonstrates a professional attitude regarding communication with relevant stakeholders (e.q. mentors, advisors, peers, and customers) an analytical attitude, work ethos, planning competence, pro-activeness and self-awareness.
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| LEARNING OUTCOME 1 |
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LEARNING OUTCOME 2 |
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LEARNING OUTCOME 3 |
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
|
Ethical focus: Research in Practice
Ethical focus: Research in Practice
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-ND 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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Guidelines for the Thesis
| Ethical guidelines |
These guidelines are intended to provide ethical guidance for the HCAIM theses.
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| HCAIM Thesis Proposals Guidelines |
These guidelines are intended to support parties which intend to propose a new thesis.
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| HCAIM Thesis Template |
View the HCAIM Thesis Template here.
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| Risks Template |
This template allows the supervisor to support the student in identifying and dealing with problems. At the same time, a thesis proposing party will be asked to compile this template.
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More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
|
Examples of Thesis Topics
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
|
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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