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


To study on a distance education will give you different opportunities than on-campus teaching. It means that, to a large extent, you will be able to plan your studies yourself, both in terms of time and place.

However, keep in mind that most distance education includes a number of compulsory digital lectures and digital seminars during the weekdays. Some distance education also include compulsory get-togethers, for which you will have to travel to Växjö or Kalmar.

There are a number of different ways to be a distance student, the common denominator being that a large part of your study work is carried out on the web. You communicate with the teacher and your fellow students using a learning platform with discussion forums, group work, recorded lectures or video meetings using a web cam.

Student working from home