Introduction to Machine Learning

7.5 credits

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The course covers machine learning concepts and methods. The following topics are covered in the course:

• basic statistical concepts

• supervised and unsupervised learning

• linear and polynomial regression,

• logistic regression

• decision trees

• Support vector machines

• unsupervised learning using the k-means clustering algorithm

• algorithm evaluation using cross-validation and mean square error

• evaluation metrics such as precision, recall, and F-score

• algorithm implementation using MATLAB


Roughly 15 minutes with a bike from the city centre, you will find Linnaeus University’s campus. It is like a small society with the university, student accommodation, and student life. Here you become part of a creative knowledge environment.

What will you come across on an excursion in Växjö – the city of contrasts? You will find good restaurants, a celebrated hockey team, and cozy cafés where you can enjoy a latte with lingonberry flavour. In Växjö, beautiful nature is always just around the corner; the city is surrounded by lakes and forests. Students like the combination of the city centre and the active student life on campus. Your dream of the future starts here!

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