Statistics: Advanced Machine Learning

Building on the foundations from STAN51 Statistics: Machine Learning from a Regression Perspective, this course introduces you to more advanced machine learning techniques with a focus on applications in business and economics.You will learn about:

• bootstrap methods for assessing model stability and uncertainty
• ensemble techniques such as boosting and random forests for improving predictive performance
• unsupervised learning methods including principal component analysis and clustering
• applied machine learning for real-world problems, including causal inference in economic and business contexts.

Course literature

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

You’ll combine theory with hands-on applications using real-world data from business and economics. You will be assessed through assignments which are presented both orally and in writing, and an exam.

After completing the course, you’ll have a strong understanding of machine learning and how to apply it in practice. If you want to explore further, you’ll be eligible to take STAN47 Statistics: Deep Learning and Artificial Intelligence Methods.

Prerequisites

90 credits in Statistics with at least 7.5 credits in Regression Analysis or Econometrics and also STAN51 Statistics: Machine Learning from a Regression Perspective, 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.