Siena, Italy

Data Science & Machine Learning

blended course
when 19 July 2021 - 23 July 2021
language English
duration 1 week
credits 8 EC
fee EUR 270

MSc students, PhD students, postdocs, junior/senior academics, and industry practitioners will be typical profiles of the attendants. In fact, the Advanced Course is not a summer school suited only for younger scholars. Rather, a significant proportion of seasoned investigators are regularly present among the attendees, often senior and junior faculty at their own institutions. The balanced audience that we strive to maintain in each Advanced Course greatly contributes to the development of intense cross-disciplinary debates among faculty and participants that typically address the most advanced and emerging areas of each topic.

Each faculty member presents lectures and discusses with the participants for one entire day. Such long interaction together with the small, exclusive Course size provides the uncommon opportunity to fully explore the expertise of each faculty, often through one-to-one mentoring. This is unparalleled and priceless.

The Certosa di Pontignano provides the perfect setting to a relaxed yet intense learning atmosphere, with the stunning backdrop of the Tuscan landscapes. World-class wines and traditional foods will make the Advanced Course on Data Science and Machine Learning the experience of a lifetime.

Course leader

Giuseppe Nicosia, University of Catania - Italy & University of Cambridge - UK
Panos Pardalos, University of Florida, USA

Target group

MSc students, PhD students, postdocs, junior/senior academics, and industry practitioners will be typical profiles of the attendants. In fact, the Advanced Course is not a summer school suited only for younger scholars. Rather, a significant prop

Course aim

In a proven, unique format you will be exposed to a high-impact learning experience, taking you outside the comfort zone of your own technical expertise, that will empower you with new analytical and strategic skills across areas of Big Data, Deep Learning and Artificial Intelligence.
The 4th Advanced Course on Data Science & Machine Learning (ACDL) is a full-immersion five-day residential Course at the Certosa di Pontignano (Siena – Tuscany, Italy) on cutting-edge advances in Data Science and Machine Learning with lectures delivered by world-renowned experts. The Course provides a stimulating environment for academics, early career researches, Post-Docs, PhD students and industry leaders. Participants will also have the chance to present their results with oral talks or posters, and to interact with their peers, in a friendly and constructive environment.
You will gain a heightened awareness for fields of data science and machine learning relevant for your activity and, perhaps most important, you will gain a place within an elite global network of data scientists and machine learning experts.

ACDL 2021 LECTURERS:
Each Lecturer will hold three/four lessons on a specific topic.

* Pierre Baldi, University of California Irvine, USA
* Jacob D. Biamonte, Skolkovo Institute of Science and Technology, Russian Federation
* Chris Bishop, Microsoft, Cambridge, UK Laboratory Director at Microsoft Research Cambridge & University of Edinburgh
* Silvia Chiappa, DeepMind, London, UK
* Oren Etzioni, Allen Institute for AI, USA CEO at Allen Institute for AI
* Marco Gori, University of Siena, Italy
* Georg Gottlob, Computer Science Dept, University of Oxford, UK
* Michael I. Jordan, University of California, Berkeley, USA
* Marta Kwiatkowska, Computer Science Dept., University of Oxford, UK
* Panos Pardalos, University of Florida, USA
* Daniela Rus, MIT, USA) Director of CSAIL (TBC)
* Silvio Savarese, Stanford, University, USA Institute for Human-Centered Artificial Intelligence
* Cristina Savin, New York University, Center for Neural Science & Center for Data Science, USA
* Naftali Tishby, Hebrew University, Israel
* Isabel Valera, Saarland University, Germany Max Planck Institute for Intelligent Systems, Tübingen, * Germany
* Mihaela van der Schaar, University of Cambridge, UK

Credits info

8 EC
The Course will involve a total of 36-40 hours of lectures, according to the academic system the final achievement will be equivalent to 8 ECTS points for the PhD Students and the Master Students attending the Course.

Fee info

EUR 270: Online Early Registration Fee
EUR 550: Onsite Early Registration Fee

Scholarships

n.a.