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2023/2024  BA-BHAAV2306U  Applied Machine Learning for Economics and Finance

English Title
Applied Machine Learning for Economics and Finance

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 BSc in Economics and Business Administration
Course coordinator
  • Jonas Striaukas - Department of Finance (FI)
Main academic disciplines
  • Finance
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 15-03-2023

Relevant links

Learning objectives
The goal of the course is to show students how machine learning (ML) methods can be applied in various business applications ranging from econometrics, finance, macroeconomics, management, and so on. The course emphasis is on applications of the methods focusing on case studies aimed at demonstrating how each method is (or is not) suitable for a particular data type. Student 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), regression analysis and some prior knowledge of statistical software R is helpful, but not required. The course is self-contained.
Applied Machine Learning for Economics and Finance:
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/or on canvas
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 and Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The exam is project-based. Students will be given a dataset and questions that need to be answered. The script of the analysis can be written in R, but students can also choose other programming languages such as Matlab, Python, etc. In this case, students should confirm with the course instructor that the programming language used in the analysis is sufficient for the exam.

Course content, structure and pedagogical approach

The course will cover different machine learning (ML) methods focusing on applications in economics and finance. The emphasis of the course is on the applications, but the methods will be introduced in a rigorous and precise way. The course topics are:


  • Lecture 1 — Introduction to statistical/machine learning and R programming
  • Lecture 2 — Predictive linear regression
  • Lecture 3 — Multiple linear regression and regularization
  • Lecture 4 — Loss function, classification. Logistic and quantile regression
  • Lecture 5 —  Guest lecturer (TBA)
  • Lecture 6 —  Principal component analysis and factor models

  • Lecture 7 — Time series models
  • Lecture 8 — Resampling methods for ML
  • Lecture 9 — Optimization methods for ML
  • Lecture 10 — Introduction to advanced topics in machine learning



Description of the teaching methods
Teaching consists of lectures and practice workshops. Each topic covers 1 hour of methodological lecture followed by 1 hour of exercises. During the exercise sessions, students will work on relevant exercises with the help of the course instructor. Recorded solutions of some selected sessions will be posted on the course website.

The exam will be based on parts of the practice exercises — typically one or more ML methods. The solution to these sessions can be discussed with the lecturer during the practice sessions, but there will be no opportunity for further feedback until the exam.

Material: slides of lectures, code scripts, course textbook. Additional material for practical sessions will be posted on the course website. An introduction to R basics will be taught during the first exercise session (Lecture 1).
Feedback during the teaching period
During the practice sessions, students will work on exercises in small groups, discussing solutions/problems with the lecturer. Recorded solutions of some selected sessions will be posted 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 15-03-2023