Statistics: Machine Learning from a Regression Perspective

Machine learning is about building models that improve as they receive more data – they learn from experience. This technology is used everywhere: from predicting customer behaviour in shops to optimising online advert pricing. In this course, you’ll get an introduction to machine learning, with a focus on methods based on regression analysis.

You’ll learn about:

• classification using logistic regression
• model selection using information criteria and cross-validation
• shrinkage methods such as lasso and ridge regression
• dimensionality reduction using principal component analysis and partial regression
• the basics of neural networks.

Course literature

The course literature listed may be updated up to eight weeks before the course begins.

Teaching combines theory with practical applications, particularly in business and economics. You’ll work with real-world data and use machine learning to solve concrete problems.

After completing the course, you’ll have a solid foundation in machine learning and be able to apply its methods in practical settings. If you wish to deepen your knowledge, we recommend the follow-up course STAN52 Statistics: Advanced Machine Learning.

Prerequisites

90 credits in Statistics with at least 7.5 credits in Regression Analysis or Econometrics, or the equivalent.

Selection criteria

Seats are allocated according to: ECTS (HPAV): 100 %.

Tuition fees for non-EU/EEA citizens

Citizens of countries outside:

• The European Union (EU)
• The European Economic Area (EEA) and
• Switzerland

are required to pay tuition fees. You pay an instalment of the tuition fee in advance of each
semester.

Tuition fees, payments and exemptions

Full programme/course tuition fee: SEK 18,750
First payment: SEK 18,750

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Note that you may also need to pay an application fee, or provide proof of exemption.

Application fee

No tuition fees for citizens of the EU, EEA and Switzerland

There are no tuition fees for citizens of the European Union (EU), the European Economic Area (EEA) and Switzerland.