For more information about the courses offered for the PhD programme in Data Science (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 | |
|---|---|---|---|---|---|
| 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 | ||
| Sebastian Goldt | Current topics in the theory of neural networks: Dynamics and Data | 42 h | April - June | 7 | |
| Roberto Trotta | Current topics in the theory of neural networks: Dynamics and Data | 42 h | April - June | 7 | |
| Guido Sanguinetti | Machine learning applications to single-cell genomics | 42 h | April - June | 7 | |
| Luca Bortolussi, Giulio Caravagna, Luca Manzoni, Lorenzo Castelli | Scientific Computing and Algorithms | 36 h | TBA | 6 |