| Mandatory Courses |
|
Data Drive Healthcare 6 ECTS - 48h |
Area – Artificial Intelligence Learning outcomes - The student will acquire the fundamental skills of understanding and managing biomedical data. This includes electronic collection, storage and exploration by means of statistical methods. |
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Introduction to Data Science 6 ECTS - 48h |
Area – Artificial Intelligence Learning outcomes - After a successful completion of the course, the student should be able to prepare the data in attribute-value format suitable for machine learning methods; for a given data set, distinguish between application of supervised and unsupervised learning; given the data, select the right method for its analysis; use feature dimensionality reduction techniques to help understand the data; use the most appropriate data visualization technique for a given problem; apply the right model evaluation and scoring approaches to assess the quality of the modeling technique; understand the necessity of explanations, and be able to explain results of unsupervised or supervised modeling; and use Orange Data Mining software for data analytics. |
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Trustworthy AI 6 ECTS - 48h |
Area – Ethical and Legal Considerations Learning outcomes – Student will gain knowledge on the foundations of Trustworthy AI and competences for realizing Trustworthy AI. Moreover, this module intends to allow students to learn how to quantitatively assess trustworthiness of AI in practice. |
| Elective Courses |
|
Coding in Python 6 ECTS - 48h |
Area – Artificial Intelligence Learning outcomes - The student will acquire basic skills of computer programming and scripting, using the Python (v3.x) programming language. |
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Text Mining 6 ECTS - 48h |
Area – Artificial Intelligence Learning outcomes – The student will acquire knowledge on the use of the core machine learning algorithms for text mining. After the completion of this course, students will be able to preprocess textual data, understand specifics of text, transform raw text to attribute-value representation and evaluate language-based models. |
|
Introduction to Healthcare Management 6 ECTS - 48h |
Area – Healthcare Management Learning outcomes - This module provides the student with a comprehensive knowledge on the management of healthcare organisations, grounded on a diversified and international perspective. The complexity of healthcare organisations requires managers to develop a set of skills aimed at simultaneously managing clinical performance, staff and financial resources to provide a better outcome for the population as a whole. Therefore, students will learn how to experience ambidexterity in managerial activity in order to improve decision-making in a multi-objective environment. Learning will be based on lectures, teamwork and case studies delivered by lecturers and practitioners working for national and foreign healthcare organisations in order to compare and contrast different managerial approaches. |
|
Advanced AI Assessment 6 ECTS - 48h |
Area – Healthcare Management Learning outcomes - Students will get an extensive knowledge about Health Technology Assessment (HTA) including the factors affecting it and the way it could be successfully implemented in different healthcare systems. In particular, they will learn how to assess AI as a strategic lever to build value-based health systems. |
| Mandatory Courses |
|
Transforming healthcare 6 ECTS - 48h |
Area – Healthcare Management Learning outcomes - Participants will gain knowledge on the challenges of the healthcare sector, at different levels of analysis, and transformational strategies to address them exploiting AI potential. |
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AI and healthcare workforce 6 ECTS - 48h |
Area – Healthcare Management Learning outcomes - Participants will gain knowledge on the challenges of the healthcare workforce, taking into consideration the evolving competences that are needed. Moreover, this course will focus on the relationship between the clinicians and the patients when adopting AI devices, considering the social and psychological aspects of computer-mediated communication. |
|
Z-Inspection ®: A Process to assess trustworthy AI in Practice 6 ECTS - 48h |
Area – Ethical and Legal Considerations Learning outcomes - The Z-Inspection® process is a formalized and principled approach for evaluating the design, deployment, and use of AI- based systems towards, aimed at ensuring that the final system iteration is both trustworthy and trusted. It is positioned within the broader trend to design and assure trustworthy AI systems. It can be used at various stages of the AI development and maintenance process. First, in the design phase, the Z- Inspection® methodology can be utilized as a co-creation process to ensure an AI system meets the trustworthy AI criteria. Both before and after AI deployment, Z-Inspection® can be used as a validation process to assess the trustworthiness of the AI system being developed. Additionally, it can form part of an AI certification, audit or monitoring process. The latter can be considered a part of “ethical maintenance” for trustworthy AI. |
| Mandatory Courses |
|
Computer Vision and Deep Learning 6 ECTS - 48h |
Area – Artificial Intelligence Learning outcomes – After the completion of this course, students will be able to list useful real-world applications of computer vision, to apply and design computer vision systems and algorithms and to evaluate appropriate computer vision algorithms for a variety of problems. |
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Advanced Topics in AI 6 ECTS - 48h |
Area – Artificial Intelligence Learning outcomes - Introduction to the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis lies on the statistical and decision-theoretic modeling paradigm. The techniques taught apply to a wide variety of artificial intelligence problems and serve as the foundation for further study in any application area. |
|
AutoML 6 ECTS - 48h |
Area – Artificial Intelligence Learning outcomes - The course on "Automated Machine Learning" addresses the challenge of designing well-performing Machine Learning (ML) pipelines, including their hyperparameters, architectures of deep Neural Networks and pre-processing. Future ML developers will learn how to use and design automated approaches for determining such ML pipelines efficiently. |
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Information Ethics and Legal Aspect 6 ECTS - 48h |
Area – Ethical and Legal Considerations Learning outcomes - Students will apply principles of information ethics to relevant scenarios and cases, develop ethical analytical skills, identify and analyze ethical issues associated with the use of AI/ML in healthcare, develop ethically reasoned solutions to issues of AI/ML in healthcare and apply various ethical theories and frameworks in analysis. |
| Mandatory Courses |
|
Internship 25 ECTS - 450h |
Learning outcomes - The student will complete the internship at a medical institution - a hospital, a research center, a laboratory or similar. The internship is completed after the submission of a report. |
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Thesis writing 5 ECTS - 48h |
Learning outcomes - MA thesis on a topic approved by the teaching committee. |