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2026/2027  BA-BHAAV2624U  Applied Machine Learning

English Title
Applied Machine Learning

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 60
Study board
Study Board for General Management
Programme Bachelor of Science in Economics and Business Administration
Course coordinator
  • Jonas Striaukas - Department of Finance (FI)
Main academic disciplines
  • Mathematics
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 30-01-2026

Relevant links
Learning objectives
The goal of the course is to demonstrate how machine learning (ML) methods can be applied across a variety of business domains, including econometrics, finance, macroeconomics, and management. The emphasis is on the practical application of these methods, with case studies designed to illustrate when and why a particular method is (or is not) suitable for a given type of data. Students will:
  • Be able to differentiate between different data types
  • Identify which method(s) are suitable to apply for a particular data problem
  • Understand basic theoretical concepts of machine learning
  • Be able to effectively interpret the results when applying different machine learning methods
  • Be aware of modern machine learning methods in prediction and causal inference applications
Course prerequisites
Familiarity with basic statistics concepts (probability, expectation) and basic regression analysis is assumed, and prior experience with the statistical software R or Python is advantageous.
Examination
Applied Machine Learning:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Project
Release of assignment An assigned subject is released in class
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The examination is project-based. Students will be provided with a dataset and a set of questions to be answered. Although it is recommended that the analysis be conducted in R, students may choose to use alternative programming languages such as MATLAB, Python, or other preferred languages. In such cases, students are required to obtain approval from the course instructor to ensure that the selected programming language is adequate for meeting the examination requirements.
Course content, structure and pedagogical approach

The course will cover a range of machine learning (ML) methods with a particular emphasis on their practical applications. While the focus is on applied aspects, all methods will be introduced in a rigorous and precise manner. The topics to be covered include:

 

1. Overview of statistical learning

2. Linear regression

3. Classification

4. Resampling methods

5. Model selection and regularization

6. Moving Beyond Linearity

7. Tree-based methods

8. Deep learning

9. Survival analysis and censored data

10. Unsupervised learning

11. Multiple testing
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • New theory
  • Methodology
  • Models
Research-like activities
  • Discussion, critical reflection, modelling
Description of the teaching methods
Teaching consists of lectures and practice workshops. Each topic includes a one-hour methodological lecture followed by a one-hour exercise session. During the exercise sessions, students will work on relevant problems with guidance from the course instructor. Recorded solutions for selected sessions will be made available on the course website.

The examination will draw on material from the practice exercises, typically involving one or more ML methods. Solutions to these exercises may be discussed with the instructor during the scheduled practice sessions; however, no additional feedback will be provided prior to the exam.

Course materials include lecture slides, code scripts, and the course textbook. Additional materials for the practical sessions will be posted on the course website. An introduction to the basics of R will be provided during the first exercise session.
Feedback during the teaching period
During the practice sessions, students will work on exercises in small groups and discuss solutions or difficulties with the instructor. Recorded solutions for selected sessions will be made available on the course website.
Student workload
Lectures 26 hours
Practice workshops 12 hours
Exam 30 hours
Preparations 140 hours
Expected literature

 

 

Advanced level book: 

  • Graduate level book which covers more recent topics in ML — Fan, J., Li, R., Zhang, C. H., & Zou, H. (2020). Statistical foundations of data science. Chapman and Hall/CRC.
Last updated on 30-01-2026