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Data Science Courses

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.

Courses of the Academic Year 2021-2022



Lecturer Title Duration Period CFU
Luca Heltai An introduction to modern tools for collaborative science (best practices in co-developing and co-authoring) 12 h October-November
Guido Sanguinetti Bayesian Inference I 36 h First term
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
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

Courses of the Academic Year 2020-2021



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
Guido Sanguinetti Machine learning applications to single-cell genomics 42 h April - June 7
Sebastian Goldt Current topics in the theory of neural networks: Dynamics and Data 42 h April - June 7

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