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 understand the concept of generalization and how to analyse it using simple probabilistic models. 

Students are familiar with linear models for regression and classification. 

Students know how basic ML methods are obtained as combinations of particular choices for data representation (features), hypothesis space (model) and loss function. 

Students are familiar with the idea of hard and soft clustering methods. 

Students understand the basic idea of feature learning methods 


Credits: 5

Schedule: 10.01.2022 - 08.04.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Alex Jung, 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:

    Components of Machine Learning: Data, Hypothesis Space and Loss Functios 

    Algorithms for Machine Learning: Gradient Descent, Greedy Search, Linear Solvers


Assessment Methods and Criteria
  • valid for whole curriculum period:

    assignments, project report

  • valid for whole curriculum period:

    5 credits approx 130 hours of work divided into 

    lectures + self-study (30 hours)

    assignments (6 * 10 = 60 hours) 

    project work (around 40 hours) 


Study Material
  • valid for whole curriculum period:

    see course page

Substitutes for Courses
SDG: Sustainable Development Goals

    1 No Poverty

    2 Zero Hunger

    3 Good Health and Well-being

    5 Gender Equality

    6 Clean Water and Sanitation

    7 Affordable and Clean Energy

    8 Decent Work and Economic Growth

    9 Industry, Innovation and Infrastructure

    10 Reduced Inequality

    11 Sustainable Cities and Communities

    12 Responsible Production and Consumption

    13 Climate Action

    14 Life Below Water

    15 Life on Land

    16 Peace and Justice Strong Institutions

    17 Partnerships for the Goals


Further Information
  • valid for whole curriculum period:

    CS-C3240 Machine Learning overlaps with CS-E3210 Machine Learning: Basic Principles and CS-EJ3211 Machine Learning with Python and only one of them can be included in the degrees. If you have already taken one of the basic machine learning courses, you should take the course CS-E4710 Machine Learning: supervised methods instead.

    Teaching Period:

    2020-2021 Spring III-IV

    2021-2022 Spring III-IV

    Course Homepage:

    Registration for Courses: Kurssille ilmoittaudutaan WebOodissa. Katso ilmoittautumisaika WebOodista. Ilmoittautumiseen