Machine Learning 7.5 credits

About the course

This course is an introduction to machine learning and provides an overview of both theoretical and practical aspects of machine learning. The course introduces the basic concepts of machine learning and presents a range of different machine learning methods and models. The course also covers statistical and practical issues related to the design and evaluation of machine learning solutions.

The course consists of two parts:

Part 1: Principles (4.5 credits)

This part introduces the background and some important applications of machine learning. The following models and topics will be covered: Supervised learning (classification and regression with methods like support vector machines, naive Bayes, K-nearest neighbours, decision trees, neural networks), unsupervised learning (clustering and dimensionality reduction with methods like k-means clustering, hierarchical clustering, principal components analysis, linear discriminant analysis, density estimation), and learning theory (PAC-learning, bias/variance trade-off, regularisation). Furthermore, important concepts in machine learning, such as generative/discriminative learning, the maximum likelihood and Bayesian learning paradigms, and parametric/non-parametric learning will be discussed.

Part 2: Practice (3 credits)

This part consists of practical assignments that introduce modern machine learning libraries and development tools. The students will apply some of the machine learning methods/models covered in Part 1 to solve machine learning problems in realistic applications.

Show syllabus

Apply

Spring 2026
- Machine Learning
  
  Spring 2026 / Umeå / English / On site
  
  Application opens 15 September 2025
  
  Save education at universityadmissions.se
  
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  Starts
  19 January 2026
  
  Ends
  23 March 2026
  
  Number of credits
  7.5 credits
  
  Type of studies
  On site
  
  Study pace
  50%
  
  Teaching hours
  Daytime
  
  Study location
  Umeå
  
  Language
  English
  
  Application code
  UMU-57316
  
  Eligibility At least 60 ECTS computing science or 120 ECTS within a program. (Students on a master's program leading to a degree in computing science are considered to fulfill this requirement.) At least 7.5 ECTS introductory programming; 7.5 ECTS data structures and algorithms; 7.5 ECTS mathematics including limits, derivatives, and probability theory; 7.5 ECTS linear algebra; 7.5 ECTS mathematical statistics.
  
  Selection
  Academic credits
  
  Application
  The online application opens 15 September 2025 at 09:00 CET. Application deadline is 15 October 2025.
  
  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 19,038
  
  Total fee: SEK 19,038
  
  Details about tuition, fees and funding

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