Each Lecturer will hold three lessons on a specific topic.
The Lecturers below are confirmed.
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.
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.
TopicsMachine Learning for Medicine, Data Science and decisions, Artificial Intelligence
Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Turing Fellow at The Alan Turing Institute in London, where she leads the effort on data science and machine learning for personalised medicine. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018). She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 35 granted USA patents.
The current emphasis of her research is on machine learning with applications to medicine, finance and education. She has also worked on data science, network science, game theory, signal processing, communications, and multimedia.
Five papers accepted at NeurIPS 2019
TensorFlow is a powerful open-source software library for machine learning developed by researchers at Google. It has many pre-built functions and models to ease the task of building and training neural networks, both on single machine and on multiple machines. TensorFlow also has dedicated modules to support the seamless integration of research with production.
This tutorial will cover the fundamentals and contemporary usage of the TensorFlow library for deep learning. It aims to help the audience understand the design choices that led to TensorFlow 1.0 and 2.0, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the tutorials, students will use TensorFlow to build models of different
complexity, from simple linear/logistic regression to convolutional neural networks, recurrent neural networks, and self-attention models to solve tasks such as word embedding, translation, optical character recognition, joint intent slot filling for conversational AI. Students will also learn the best practices to structure a model and manage research experiments.
Over the past two years PyTorch has became the dominant tool for machine learning research, with many of the groundbreaking advancements appearing alongside their PyTorch implementations. Having at least a basic understanding of the library is an asset, as it allows one to easily collaborate with others, develop their own research faster or at least gain a deeper understanding of the resources published every day online.
This course will be a gentle introduction to the PyTorch library and we will go over all of its fundamental abstractions. Those include the way model code is usually structured, how can one go about computing gradients of arbitrary Python functions automatically, making effective use of accelerators such as GPUs and what are the best practices for research implementations. If time allows, we will also take a peek into some more advanced features like the just-in-time compiler.
The Lecturers of the previous editions:
- Ioannis Antonoglou, Google DeepMind, UK
- 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
- Giuseppe Di Fatta, University of Reading, UK
- Marco Gori, University of Siena, Italy
- Yi-Ke Guo, Imperial College London, UK & Founding Director of Data Science Institute
- Phillip Isola, MIT, USA
- Leslie Kaelbling, MIT - Computer Science & Artificial Intelligence Lab, USA
- Ilias S. Kotsireas, Wilfrid Laurier University, Canada
- 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
- Dolores Romero Morales, Copenhagen Business School, Denmark
- Ruslan Salakhutdinov, Carnegie Mellon University, and AI Research at Apple, USA
- Josh Tenenbaum, MIT, USA
- Naftali Tishby, Hebrew University, Israel
- Joaquin Vanschoren, Eindhoven University of Technology, The Netherlands
- Oriol Vinyals, Google DeepMind, UK
- Aleskerov Z. Fuad, National Research University Higher School of Economics, Russia