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Machine learning

  • Number of credits 7.5 credits
  • Level Master’s level
  • Starting Spring Term 2023

About the course

The course introduces the basic grounding in concepts and a range of model based and algorithmic machine learning methods, as well as practical design and evaluation of some machine learning solutions.
The course is divided into three parts:
Part 1: Principles, 4 credits.
This part introduces the background and important applications of machine learning. The following topics will be covered: support vector machines, naive bayes, k-nearest neighbor, decision trees, neural networks, linear regression, clustering, hidden markov model, and reinforcement learning. Meanwhile, a series of important concepts and knowledge will be mentioned including bias/variance tradeoffs, generative/discriminative learning, kernel methods, parametric/non-parametric learning, graphic models, and deep learning. Some machine learning libraries (e.g. scikit- learn) and development tool will be briefly introduced.
Part 2: Seminar, 0.5 credits.
Research papers on some specific machine learning topics will be provided in advance to all the students for reading. In this part, the students are divided into groups and are required to give short presentations to explain and assess the assigned paper (i.e. the proposed solution, datasets, experimental design etc.).  One group representative can be chosen to present but all group members must be present and involved in discussion. The teacher and other students will ask questions and give feedback.
Part 3: Practice, 3 credits.
This part consists of 2-4 mandatory practical assignments. The students are required to apply some of the machine learning algorithms/models and frameworks/libraries from Part 1 into practice by solving the assigned problems in real applications (e.g. text classification, search engine, or recommendation system)

Application and eligibility

Machine learning, 7.5 credits

Det finns inga tidigare terminer för kursen Spring Term 2023 Det finns inga senare terminer för kursen


16 January 2023


21 March 2023

Study location




Type of studies

Daytime, 50%

Required Knowledge

Univ: To be admitted you must have (or equivalent) 90 ECTS-credits including 60 ECTS-credits in Computing Science or two years of completed studies within a study programme (120 ECTS-credits). In both cases, including at least 7.5 ECTS-credits in artificial intelligence (e.g. 5DV121) and at least 7.5 ECTS-credits in mathematical statistics, (e.g. 5MS045).

Proficiency in English equivalent to Swedish upper Secondary course English A/5. Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies.

Entry requirements


Academic credits Applicants in some programs at Umeå University have guaranteed admission to this course. The number of places for a single course may therefore be limited.

Application code



Application deadline was 17 October 2022. Please note: This second application round is intended only for EU/EEA/Swiss citizens. Submit a late application at Universityadmissions.se.

Application and tuition fees

As a citizen of a country outside the European Union (EU), the European Economic Area (EEA) or Switzerland, you are required to pay application and tuition fees for studies at Umeå University.

Application fee

SEK 900

Tuition fee, first instalment

SEK 17,850

Total fee

SEK 17,850

Contact us

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Course is given by
Department of Computing Science