For more information about the courses offered for the PhD programme in Data Science (TS-DS), please visit the webpage of the programme. The formal learning opportunities will be flanked with a vigorous programme of online seminars (the “SISSA Data Science Seminar Series”, or SISSA DS 3 ), held approximately fortnightly from January 2021, with a focus on showcasing a young and diverse line-up of world-class speakers from all over the world. Further details will be published on this webpage.
Students from other PhD programmes who are interested in following our modules are requested to register their interest by filling out this form. This is for logistical (especially in view of COVID-19 restrictions to teaching spaces) and pedagogical reasons. Deadline is Fri Oct 2nd 2020.
Please, notice that MAMA PhD students can plan 33% of their credits (i.e. 50 hrs) from the courses listed below.
| Lecturer | Title | Duration | Period | CFU | |
|---|---|---|---|---|---|
| Luca Heltai | An Introduction to modern tools for collaborative science | 12 h | October - December | ||
| Luca Heltai, Gianluigi Rozza | Applied Mathematics: an Introduction to Scientific Computing by Numerical Analysis | 48 h | October - December | 6 | Second Year |
| Lecturer | Title | Duration | Period | CFU | |
|---|---|---|---|---|---|
| Roberto Trotta | Bayesian Inference II | 36 h | Second term | ||
| Roberto Trotta as lead, plus guest lecturers | Ethics in ML and AI | 20 h | All year | ||
| Jean Barbier | Information Theory, Spin Glasses and Inference | 36 h | Second term | ||
| Nicoletta Krachmalnicoff | Introduction to Statistical Modelling and Inference | 24 h | First term | ||
| Roberto Trotta | Monographic: Bayesian inference and machine learning in cosmology | 20 h | Third term | ||
| Sebastian Goldt | Monographic: Current topics in the theory of neural networks: Dynamics and Data | 20 h | Third term | ||
| Guido Sanguinetti | Monographic: Machine learning in high-throughput biology | 20 h | Third term | ||
| Guido Sanguinetti | Bayesian Inference I | 36 h | First term | ||
| Alessandro Laio, Alex Rodriguez | Unsupervised Learning and Non-parametric Methods | 38 h | First term | ||
| Sebastian Goldt , Alessandro Treves, Antonio Celani | Neural Networks | 36 h | Second term | ||
| Alessandro Treves, Carlo Baccigalupi, Stefano de Gironcoli, Mathew Diamond, Antonio Celani | Applications of Data Science to Natural Sciences | 18 h | first and second terms | ||
| Luca Heltai | An introduction to modern tools for collaborative science (best practices in co-developing and co-authoring) | 12 h | October-November |
| Lecturer | Title | Duration | Period | CFU | |
|---|---|---|---|---|---|
| Andrea De Simone | Introduction to Statistical Modelling and Inference | 26 h | October | ||
| Guido Sanguinetti | Bayesian inference I | 36 h | October - December | 6 | |
| Jean Barbier | Information Theory and Inference | 26 h | November - December | ||
| Roberto Trotta | Bayesian Inference II | 36 h | January - February | 6 | |
| Alessandro Laio, Alex Rodriguez | Unsupervised Learning and non-Parametric methods | 38 h | January - February | ||
| Sebastian Goldt | Neural Networks | 36 h | January - March |