This course is part of the new M.Sc. in Artificial Intelligence, developed as part of the Master programmes in Artificial Intelligence 4 Careers in Europe, and is offered within the network of partner universities promoting new careers in AI for students from any EU country.
The course aspires to help students explore and master key concepts and challenges of relevance to AI and Data-driven entrepreneurship. It introduces students to the world of high-technology entrepreneurship through case studies that demonstrate successes, failures, and challenges. The course provides also an overview of, and an introduction to key steps to develop a start-up, design a business model, explore product-market fit, manage intellectual property, and attract investment.
Students will explore acknowledged innovation-driven entrepreneurship methodologies and experiment with them and associated tools to pursue the translation of their ideas into entrepreneurial endeavors. The course examines issues faced by Start-up Founders and Chief Technology Officers who need to innovate at the boundaries of AI, Ιnformation Τechnology, and Βusiness by understanding all perspectives.
- Teacher: Marios Dikaiakos
The medical domain has presented key challenges to the AI community
from the early days of AI research. It is not an exaggeration to say that this
pioneering work, particularly in medical expert systems, and its undisputable
successes, some in real-life settings, has helped both in restoring
confidence in the promise of AI, that at some point was disturbed after its
failure to deliver fully on the very ambitious initial goals that it had set, and in
paving the way towards more viable paths harnessing the mechanization of
knowledge and human expertise. AI in Medicine (AIM) is as old as AI itself,
initially focusing on modelling human expertise, researching at the same
time the cognitive processes involved in developing from a novice to an
expert problem solver, as well as on intelligent tutoring systems for medical
students and automated support for various clinical tasks. Over the years
the initial focus on knowledge engineering has expanded to include
ontologies and terminologies, natural language processing and text mining,
guidelines and protocols, temporal information management, distributed and
cooperative systems, uncertainty management, machine learning, image
and signal processing and others. Recent interest focuses on medical
analytics for healthcare intelligence. New challenges continuously arise,
triggered from and/or triggering, important technological advances. The aim
of this elective course is to familiarize students with the past, present and
future of Artificial Intelligence in Medicine, illustrating the discussion with a
number of case studies, and pinning down the human-centric and ethical
aspects underlying the given applications.
- Teacher: Elpida Keravnou
- Teacher: Kalia Orfanou
This course will offer an introduction to machine learning algorithms, the use of deep learning and its applications in computer vision and graphics. The course will also operate as a graduate-level seminar with weekly readings (1 hour per week), summarizations, and discussions of recent papers.
- Teacher: Andreas Aristidou
- Teacher assistant: Yiangos Georgiou