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Embark on a transformative journey into the realm of Artificial Intelligence and Machine Learning in this fascinating Subject Spotlight. In episode one, Alessandro introduces AI, machine learning, and what it means to work with data. In episode two, Alessandro thinks about circumstances and industries where AI and machine learning have been used for advancements: self-driving cars, cancer, and fraud detection. In episode three, Alessandro uses stroke analysis as the basis of his case study into how machine learning and AI work in practice. In the final episode, Alessandro explores the role of AI and Machine Learning in analysing data, delving into concepts like predictions versus inferences, the synergy between humans and machines, and the ethical considerations shaping the future of AI technology. In the second part, he presents the activity on “Heart Disease Data '', and finally he explores key career opportunities in the field of AI and Machine Learning.
Dr. Alessandro Di StefanoTeesside UniversityComputer science
All episodes
1. Introduction to AI & machine learning
2. Exploring challenges within AI & machine learning
3. Case study: machine learning in practice - stroke prediction
4. Activity: hands-on machine learning - analysing heart disease data
About the university
Teesside University
Based on its vibrant campus in the heart of Middlesbrough in North East England, Teesside University is a friendly, modern university offering a broad portfolio of undergraduate and postgraduate courses spanning a wide range of subject areas. The university has invested more than £280m in its town-centre campus, giving it a leading edge in digital innovation, forensic facilities, science, and sport and health sciences. It is also investing an additional £300m until 2027 to further enhance the student experience. Teesside University has received outstanding praise for its commitment to students – achieving gold in all areas in the Teaching Excellence Framework 2023. It has also been named university of the year for social inclusion in The Times and The Sunday Times Good University Guide 2022, indicating the strength of its student community and the success of the university in cultivating a supporting learning environment, with plenty of opportunities for socialising and personal enrichment beyond the classroom.
Meet the academic
Dr. Alessandro Di Stefano
Dr. Alessandro Di Stefano is a Senior Lecturer in Computer Science, within the School of Computing, Engineering, & Digital Technologies, Department of Computing & Games, at Teesside University. He received his BSc (2009) and MSc degrees (2012) in Telecommunications Engineering from the Department of Electrical, Electronic and Computer Engineering (DIEEI) at the University of Catania, Italy. He holds a PhD in Systems Engineering from the DIEEI at the University of Catania, Italy, awarded in 2015. His thesis was titled "Evolutionary Dynamics of Social Behaviours on Multilayer Networks". After his PhD, Alessandro worked as postdoctoral researcher at DIEEI, University of Catania, for almost four years, and then as a research associate and teaching assistant at the Department of Engineering, King’s College London (KCL), London, UK, supervised by Prof. Nishanth Sastry. Recently, he worked as a research associate at Computer Laboratory, Department of Computer Science and Technology, University of Cambridge, UK, supervised by Prof. Pietro Liò, with whom he has been collaborating since 2012. Alessandro is an IEEE member and part of the IEEE Professional Communication Society and the Technical Committee on Computational Life Sciences, and also member of the Complex Systems Society (CSS). He collaborates with several universities in Europe and all over the world, such as Missouri University of Science and Technology, USA. He regularly serves as a reviewer for various top-tier journals, including IEEE Transactions on Mobile Computing, Nature Scientific Reports, IEEE Transactions on Communications, IEEE Internet of Things Journal, and PloS One, and leading international conferences (IJCAI, KDD, CIKM, ICDCS, ICDCN, etc.), where he has often been invited to join the Technical Program Committee of these events. Dr. Di Stefano's research approach is interdisciplinary, incorporating engineering and computer science methodologies applied to socio-technological systems. His main research interests include game theory, network science and machine learning/artificial intelligence. He has published many peer-reviewed papers in high impact journals and leading international conferences. More recently, he has been also focusing on methodologies bringing together network science, game theory and machine learning.
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