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.


Students can formalize applications as ML problems and solve them using basic ML methods.

Students can perform basic exploratory data analysis.

Students understand the meaning of the train-validate-test approach in machine learning.

Students can apply standard regression and classification models on a given data set.

Students can apply simple clustering and dimensionality reduction techniques on a given data set.

Students are familiar with and can explain the basic concepts of reinforcement learning.

Credits: 5

Schedule: 04.09.2023 - 13.10.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Pekka Marttinen, Stephan Sigg

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:

    Exploratory data analysis.

    Dimensionality reduction, PCA.

    Regression and classification.


    Deep learning.

    Reinforcement learning.

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Assignments, project report, participation in peer-grading.

  • valid for whole curriculum period:

    5 credits approx. 134 hours of work divided into 

    Lectures + self-study: 10*(2+3)=50 hours

    Assignments: 6 * 9 = 54 hours

    Project work: 26 hours

    Peer-grading: 4 hours


Substitutes for Courses


Further Information
  • valid for whole curriculum period:

    Teaching Language : English

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