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Cand.merc.(mat.) – erhvervsøkonomi og matematik

Pre­dict­ive Mod­el­ing og Ma­chine Learn­ing

About the course

Subjects
Statistics Quantitative methods Mathematics

Course content

The course provides an introduction to the topics that are often referred to as artificial intelligence, data mining, and machine learning. During the course, the focus is going to be on the understanding of the methods and their theoretical foundations, as well as on concrete data applications of the various methods.

 

Specifically, the course covers the following topics:

 

  • Additive models
  • Penalized regression and classification: Properties, model selection, and inference
  • Resampling methods, including cross-validation
  • Tree-based models: Bagging, random forests, and boosting
  • Neural networks, including structural networks such as recurrent and convolutional neural networks
  • Selected topics on causal machine learning
  • Conformal prediction
See course description in course catalogue

What you will learn

At the end of the course, students are expected to be able to

  • explain and justify the main methods within machine learning
  • apply the most important machine-learning methods in specific problems using relevant statistical software
  • rigorously describe the strengths and weaknesses of the different methods
  • choose between different methods in a concrete data problem and justify this choice
  • communicate the results of an analysis performed with machine-learning methods

Course prerequisites

The course is aimed at students whose prerequisites correspond to an HA (math.). The teaching is in English, so adequate English is a prerequisite as well.

Facts

Level
Master
Type
Elective
ECTS
7.5
Code
CMECV1247U
Teaching periods
Spring – semester
Academic year
2025/2026
Status
Available places
Teaching language
English
Teaching Format
Hybrid
Exams
  • Oral exam on campus
    Individual exam, summer
Assessment type
  • 7 point grading scale