Teaching Material

Software Engineering and Machine Learning Workshop (European Network for Business and Industrial Statistics)

  • The Anatomy of a Machine Learning Pipeline. [ slides (pdf) | code ]
    2023–2024.

Graduate Teaching (M.Sc. in Engineering, Osaka University)

  • Advanced Data Science I: Bayesian Networks and Missing Data. [ html ]
    2023–2024.

Graduate Teaching (M.Sc. in Engineering, Scuola Universitaria Professionale della Svizzera Italiana)

  • Advanced Probabilistic Modelling. [ ]
    2022–2023 and 2023–2024.
  • Uncertain Reasoning: Machine Learning and Bayesian Networks. [ slides (pdf) ]
    2019–2020, 2020–2021 and 2021–2022.

Graduate Teaching (Ph.D. in Systems Medicine, European School of Molecular Medicine)

  • I teach part of the Machine Learning course using the material from my “Uncertain Reasoning” course and the Use R! tutorial from 2019.
    2020–2021, 2021–2022 and 2022–2023.

Advanced Summer School in Economics and Econometrics (University of Crete)

  • Bayesian Networks in Policy and Society [ slides (pdf) ]
    2021–2022

Graduate Teaching (M.Sc. / Ph.D., Università Cattolica di Milano)

  • Understanding Bayesian Networks with Examples in R. [ slides (pdf) ]
    2016–2017.

Graduate Teaching (M.Sc. in Applied Statistics/Statistical Science, University of Oxford)