Short Courses

Ethical, Legal and Human Aspects of AI and Cybersecurity

This course provides the fundamentals for ensuring the responsible governance of information technology systems with a particular focus on Artificial Intelligence (AI) and other cyber-physical systems. Students examine the societal implications of AI across various domain, fostering critical thinking and ethical decision-making skills. In particular, it explores issues such as accountability, control, privacy, and transparency in AI technologies.

Moreover, an overview of the global landscape of AI governance initiatives is provided before delving further into EU-centric legislative efforts (e.g. AI Act, GDPR, etc). Topics related to security and robustness for the safe use of the technology are also studied. Through hands-on case studies and discussions, students gain a nuanced understanding of the complex interplay between technology, ethics, and society, preparing them to navigate the ethical, legal, and social challenges posed by AI in their professional endeavours.

Fundamentals of Artificial Intelligence 

This course covers the definition, historical development, and prominent role of AI in computer science research. Participants will delve into key AI concepts and terminology, gaining practical skills in data handling, cleaning, and feature engineering. The course will explore various supervised and unsupervised learning methods, including linear regression, logistic regression, decision trees, ensemble methods, clustering techniques, and dimensionality reduction. Emphasis will be placed on practical applications using tools and libraries for building, testing, and analysing models. Additionally, the course will cover data visualisation techniques and explore trends and innovations in AI research, equipping participants with a solid foundation in AI concepts and practical skills for real-world applications.

Business Analytics

Course topics include data quality, governance, process automation using solutions as RPA, and statistical techniques such as regression analysis and hypothesis testing. The curriculum covers time series analysis, forecasting methods, and explores linear and nonlinear optimisation, including integer programming. Decision support through data analytics is addressed, covering areas as customer segmentation, supply chain analytics, and spatial analytics. A/B testing, key metrics for AI projects, and a review of Business Intelligence tools are included, along with an examination of the latest advancements in business analytics

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