Machine Learning with R, part 2

3 credits
Bachelor's level
Web-based (online)
Spring Term 2025

About the course

If you are interested in data-driven methods for predicting or decision-making, then this course is for you. The course focuses on advanced non-linear machine learning models both for tabular data and image data. If you have already taken the course Machine Learning with R, part 1, this is the perfect opportunity to deepen your skills in the rapidly evolving field of machine learning. However, it can also be taken as a standalone course since basic ideas and concepts of machine learning are reviewed during the first parts of the course. Ultimately, you will acquire knowledge about machine learning and will be able to apply these skills to real-world problems.

Machine learning with R, part 2, is running at 50% study pace and is given in English.

Syllabus

Application and eligibility

Machine Learning with R, part 2, 3 credits

Spring Term 2025

The information below is only for exchange students

Starts

20 February 2025

Ends

24 March 2025

Study location

Varied

Language

English

Type of studies

Daytime, 50%, Distance

Number of mandatory meetings

Number of other meetings

None

Required Knowledge

7,5 ECTS in Statistics incl. R, e.g., Machine Learning with R, part 1 (7.5 ECTS). Proficiency in English equivalent to Swedish upper secondary course English B/6

Selection

Students applying for courses within a double degree exchange agreement, within the departments own agreements will be given first priority. Then will - in turn - candidates within the departments own agreements, faculty agreements, central exchange agreements and other departmental agreements be selected.

Application code

UMU-A2790

Application

This application round is only intended for nominated exchange students. Information about deadlines can be found in the e-mail instruction that nominated students receive. The online application opens 26 August 2024 at 08:00 CET.

Contact us

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Course is given by

Department of Statistics, Umeå School of Business, Economics and Statistics

Contactpersons for the course are:

Study advisor, Department of Statistics, Umeå School of Business, Economics and Statistics