Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


Credits: 2

Schedule: 01.11.2021 - 17.12.2021

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Christos Merkatas, Simo Särkkä

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • applies in this implementation

    Postgraduate level knowledge in the field of Bayesian nonparametrics with applications in machine learning. The course is arranged in seminar form.

Assessment Methods and Criteria
  • applies in this implementation

    The grade is pass/fail. 

    In every seminar talk, there is the author who writes a 2 page summary paper about the subject and gives the talk, and an opponent, whose task is to make questions and stimulate discussion after the presentation. Every student is the author for one talk and the opponent for another talk. The author should send the summary paper to the opponent and to the teachers on the "to be specified" preceding the presentation day. The slides should be sent to the teacher latest on the day preceding the presentation. The opponent prepares at least 2-3 questions about the topic to stimulate discussion after the talk. Normal talk is 20-30 minutes. 

    To pass the course, attendance to all lectures/seminars is compulsory.


Study Material
  • applies in this implementation

    Two reference books (below) and recent research articles in Bayesian nonparametrics

    1. Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.). (2010). Bayesian nonparametrics (Vol. 28). Cambridge University Press.
    2. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. Third edition Chapman and Hall/CRC. Ch. 21-23

    Prerequisites: Basic notions of single/mulit-parameter Bayesian models, exponential family of distributions, conjugate and non-conjugate models and basic models of machine learning (regression, classification, clustering etc).

Substitutes for Courses