Each Lecturer will hold three lessons on a specific topic.
The Lecturers below are confirmed.
TopicsGeneral Reinforcement Learning Algorithms
TopicsTheory of Machine Learning, Generative Adversarial Networks & Theoretical Deep Learning
TopicsData Science & Knime
TopicsConstraint-Based Approaches to Machine Learning
Marco Gori received the Ph.D. degree in 1990 from Università di Bologna, Italy, while working partly as a visiting student at the School of Computer Science, McGill University – Montréal. In 1992, he became an associate professor of Computer Science at Università di Firenze and, in November 1995, he joint the Università di Siena, where he is currently full professor of computer science. His main interests are in machine learning, computer vision, and natural language processing. He was the leader of the WebCrow project supported by Google for automatic solving of crosswords, that outperformed human competitors in an official competition within the ECAI-06 conference. He has just published the book “Machine Learning: A Constrained-Based Approach,” where you can find his view on the field.
He has been an Associated Editor of a number of journals in his area of expertise, including The IEEE Transactions on Neural Networks and Neural Networks, and he has been the Chairman of the Italian Chapter of the IEEE Computational Intelligence Society and the President of the Italian Association for Artificial Intelligence. He is a fellow of the ECCAI (EurAI) (European Coordinating Committee for Artificial Intelligence), a fellow of the IEEE, and of IAPR. He is in the list of top Italian scientists kept by VIA-Academy.
TopicsGenerative Adversarial Networks
TopicsData Mining and High-Performance Computing
TopicsOptimization, Networks & Data Science
Panos M. Pardalos serves as distinguished professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. He is also an affiliated faculty member of the computer and information science Department, the Hellenic Studies Center, and the biomedical engineering program. He is also the director of the Center for Applied Optimization. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.
TopicsComputational Cognitive Science, probabilistic generative models, and probabilistic programming
TopicsTheory of Deep Learning – Information Bottleneck
TopicsMachine Learning, Computational Neuroscience, Artificial Intelligence, Theory of Computation
TopicsAutomatic machine learning
TopicsDeep Learning & Reinforcement Learning
The Lecturers of the previous editions:
- Roman Belavkin, Middlesex University London, UK
- Yoshua Bengio, Head of the Montreal Institute for Learning Algorithms (MILA) & University of Montreal, Canada
- Sergiy Butenko, Texas A&M University, USA
- Marco Gori, University of Siena, Italy
- Yi-Ke Guo, Imperial College London, UK & Founding Director of Data Science Institute
- Peter Norvig, Director of Research, Google
- Panos Pardalos, University of Florida, USA
- Alex 'Sandy' Pentland, MIT & Director of MIT’s Human Dynamics Laboratory, USA
- Marc'Aurelio Ranzato, Facebook AI Research Lab, New York, USA
- Aleskerov Z. Fuad, National Research University Higher School of Economics, Russia