Machine Learning

5 credits

Covers fundamental concepts in statistical learning and machine learning. It includes topics such as linear regression, classification methods, resampling methods, model selection and regularization, non-linear models, tree-based methods, support vector machines, unsupervised learning, and more. The course provides a comprehensive introduction to the theoretical and practical aspects of statistical learning, with a focus on real-world applications and examples. It also trains ML related R/Python programming skills and exercises to reinforce learning and facilitate practical implementation of the concepts discussed. Overall, it serves as a valuable resource for students, researchers, and practitioners interested in statistical learning and its applications in various fields.

Växjö – the student city with a living campus

Just south of Växjö’s city centre you will find Linnaeus University’s campus. In Växjö, many students choose to live on campus.

Here you will have five minutes’ walking distance to friends, restaurants, lecture rooms, gym, grocery store, the University Library, nature trails, and student pubs. If you live in another part of Växjö, you can quickly get to the university by bus or bicycle.

With its 90 000 inhabitants, Växjö is one of the fastest growing cities in Sweden. Here you will find the famous Småland entrepreneurship spirit and a lot of companies, for instance within the IT industry – perfect for when you are looking for an internship position or a job. Växjö is also a picturesque city where you are always close to forests and water.

Learn more about our student cities.

Students in house M

Build your own degree

Did you know that you can combine single-subject courses to build your own degree? In this way, you can design your own degree based on your interests and the career you are aiming for. This does not apply to all courses so make sure to check with a study counsellor at the faculty. Learn more about how you can build your own degree and become unique on the labour market.