Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), 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 the course, the student knows how to recognize and formalize supervised machine learning problems, how to implement basic optimization algorithms for supervised learning problems, how to evaluate the performance supervised machine learning models, and has understanding of the statistical and computational limits of supervised machine learning, as well as the principles behind commonly used machine learning models.

Credits: 5

Schedule: 08.09.2020 - 18.12.2020

Teacher in charge (valid 01.08.2020-31.07.2022): Juho Rousu

Teacher in charge (applies in this implementation): Juho Rousu

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

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English


  • Valid 01.08.2020-31.07.2022:

    Generalization error analysis and estimation; Model selection; Optimization and computational complexity; Linear models; Support vector machines and kernel methods; Boosting; Feature selection and sparsity; Multi-layer perceptrons; Multi-class classification; Ranking; Multi-output learning

  • Applies in this implementation:

Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    Exercises and course exam


Study Material
  • Valid 01.08.2020-31.07.2022:

    Lecture slides and exercises.

    Supplementary reading:

    • Shalev-Shwartz, Ben-David: Understanding Machine Learning, Cambridge University Press
    • Mohri, Rostamizadeh, Talwakar: Foundations of Machine Learning


Substitutes for Courses
  • Valid 01.08.2020-31.07.2022:

    Is in overlap with Autumn 2019 instance of Machine Learning: Basic Principles. 

  • Valid 01.08.2020-31.07.2022:

    CS-C3190 Machine Learning, or MS-C1620 Statistical inference, or equivalent knowledge

    Basics of probability theory

    Basic linear algebra 

    Programming skills

SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure



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