Below you can find information, videos, and slides for some of the tutorials and courses I have given.

• Toward a taxonomy of trust for probabilistic data analysis
  • Video, full information, and slides. 2024 March.
• Introduction to Machine Learning
  • Video, full information, and slides. 2020 Fall Semester, MIT's 6.036 course
• Variational Bayesian inference and beyond
  • Video, full information, and slides. 2020 August 16--21, (Online) Summer of Machine Learning at Skoltech
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    • 2018 July 10. International Conference on Machine Learning (ICML 2018), Stockholm, Sweden. Video, full information, and slides can be found here.

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    • 2024 June 21. 2024 AstroAI Workshop, Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA. Full information and slides can be found here.
    • 2021 June 13. Business and Industrial Statistics Workshop, Statistical Society of Canada (SSC) 2021 Annual Meeting. Full information and slides can be found here.
    • 2020 March 26. Virtual visit to University of Michigan, USA. Full information and slides can be found here.
    • 2020 January 15. University of Cambridge, UK. Full information and slides can be found here.
    • 2019 December 6. Tutorials on Sampling and Variational Inference during the Special Year on Statistical Machine Learning at Columbia University in the City of New York, NY, USA. Full information and slides can be found here.
    • 2019 August 14. Uncertainty Quantification Summer School at the University of Southern California (USC), CA, USA. Full information and slides can be found here.
    • 2019 January 21. Queensland University of Technology, Brisbane, Australia. Full information and slides can be found here.
    • 2018 Oct 1. 33 Foro Nacional de Estadística (FNE) y 13 Congreso Latinoamericano de Sociedades de Estadística (CLATSE), Guadalajara, Mexico. Full information and slides can be found here.
    • 2018 Sept 14--15. FoMICS-DADSi Summer School on Data Assimilation, Università della Svizzera italiana (USI), Lugano, Switzerland. Full information and slides can be found here.
    • 2018 June 4--6. Gatsby Computational Neuroscience Unit, University College London, UK. Full information and slides can be found here.
    • 2018 January 9--16. Bocconi University, Milano, Italy. Full information can be found here.

• Nonparametric Bayesian methods
  • Video, full information, and slides. 2016 May 16, Machine Learning Summer School, Cádiz, Spain
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    • 2017 January 25. Simons Institute, UC Berkeley, USA. Videos, full information, slides, and code can be found here.

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    • 2020 January 15--17. University of Cambridge, UK. Full information and slides can be found here.
    • 2019 August 18. MIFODS Workshop on Graphical models, exchangeable models, and graphons, MIT, USA. Full information, slides, and code can be found here.
    • 2019 January 22--23. Queensland University of Technology, Brisbane, Australia. Full information and slides can be found here.
    • 2018 June 19--20. Machine Learning Summer School (MLSS), Buenos Aires. Full information, slides, and code can be found here.
    • 2017 October 7. Pontificia Universidad Católica de Chile, Santiago, Chile. Full information, slides, and code can be found here.
    • 2017 July 10--14. SIdE Summer School of Econometrics, Bank of Italy Sadiba Center, Perugia, Italy. Full information, slides, and code can be found here.
    • 2017 May 30. Princeton University, USA. Full information, slides, and code can be found here.
    • 2017 May 17. Broad Institute, MIT, USA. Full information, slides, and code can be found here.
    • 2017 January 9, 13. MIT Lincoln Labs, USA. Full information, slides, and code can be found here.
    • 2016 June 12. ISBA World Meeting Short Courses, University of Cagliari, Sardinia, Italy. Full information, slides, and code can be found here.
    • 2015 August 17, 18, 19. MIT, Cambridge, MA. Full information, slides, and code can be found here.
    • 2015 July 20, 21, 24. Machine Learning Summer School, Tübingen, Germany. Videos, full information, slides, and code can be found here.

• Clusters and features from combinatorial stochastic processes [toggle abstract]

  An introduction to Bayesian cluster models with an unbounded number of potential clusters---as well as a generalization of clustering in which each data point can belong to multiple latent groups. The tutorial places particular emphasis on the following popular nonparametric Bayesian constructions: Chinese restaurant processes, Indian buffet processes, Dirichlet processes, and beta processes. The talk assumes some existing knowledge of Bayes theorem and Bayesian statistics as well as passing knowledge of Markov Chain Monte Carlo algorithms.

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    • [slides pdf part 1]
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• Machine learning crash course part II: clustering [toggle abstract]

  This talk introduces clustering as a subfield of machine learning with an emphasis on practical usage. We cover the K means algorithm, cluster evaluation, different meanings of clustering, and data pre-processing. A final example illustrates how the ideas of the lecture come together when tackling real data.

  • [youtube video: 2012 August 21]. AMP Camp: Big Data Bootcamp, UC Berkeley, California, USA.
  • [slides pdf]