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.


After completing the course, a student can: (I) explain main concepts and approaches related to decision making and learning in stochastic time series systems; (ii) read scientific literature to follow the developing field; (iii) implement algorithms such as value iteration and policy gradient.

Credits: 5

Schedule: 05.09.2022 - 30.11.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Joni Pajarinen

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


  • valid for whole curriculum period:

    Modeling uncertainty. Markov decision processes. Model-based reinforcement learning. Model-free reinforcement learning. Function approximation. Policy gradient. Partially observable Markov decision processes.

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Assignments and project work.

  • valid for whole curriculum period:

    Contact teaching, independent study, assignments, project

    Contact teaching 56 h

    Independent study 74 h


Substitutes for Courses


Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn I - II
    2023-2024 Autumn I - II

    Enrollment :

    Registration for Courses on Sisu (