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2026/2027  KAN-CMECV1247U  Predictive Modeling and Machine Learning

English Title
Predictive Modeling and Machine Learning

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for Finance, Economics & Mathematics
Programme MSc in Business Administration and Mathematical Business Economics
Course coordinator
  • Jonas Striaukas - Department of Finance (FI)
Main academic disciplines
  • Mathematics
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 26-01-2026

Relevant links
Learning objectives
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.
Examination
Predictive Modeling and Machine Learning:
Exam ECTS 7,5
Examination form Oral exam
Individual or group exam Individual exam
Duration 30 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Preparation time No preparation
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

At the oral exam, a topic is drawn (randomly) from a list of known topics. The student can choose to let a presentation of a solution of a corresponding exercise sheet be part of the oral presentation.

 

The exam is in English.
Course content, structure and pedagogical approach

This course provides an introduction to key concepts and methods within artificial intelligence and machine learning. The emphasis is placed both on understanding the theoretical foundations of the methods and on applying them to real-world data problems. Students will gain experience with model development, evaluation, and interpretation, as well as with modern approaches to statistical learning and predictive modeling.

 

Specifically, the course covers the following topics:

 

Foundations of Statistical Learning

  • Regularized regression models (e.g., ridge, Lasso)

  • Extensions to generalized linear models (e.g., logistic regression, Poisson regression)

  • Additive and nonparametric models

  • Model selection and evaluation, cross-validation

 

Machine Learning Algorithms

  • Ensemble learning: bagging, random forests, and boosting

  • Neural networks and deep learning fundamentals

 

Uncertainty, Inference, and Validation

  • Statistical inference using Lasso and multiple testing

  • Conformal prediction and distribution-free uncertainty quantification

 

Advanced Topics

  • Causal machine learning and treatment effect estimation

  • Applied considerations and case studies across real-world datasets
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
  • Classic and basic theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Analysis
  • Discussion, critical reflection, modelling
  • Peer review including Peer-to-peer
Description of the teaching methods
The course consists of lectures and exercise classes. During the exercise classes, work is done on exam-relevant exercises with the help of the course's teacher. These assignments represent an exam topic — typically one or more machine-learning methods. The solution to these tasks can be discussed with the teachers during the exercise lessons, but there will be no opportunity for further feedback until the exam.
Feedback during the teaching period
During the exercise classes, exercises are worked on in small groups in a constructive dialogue with the teachers. Each group should submit their answers to the excercise no later than 1 week after the class. The lecturer will distribute the solutions among class peers and ask each group to evaluate and give their assessment/feedback. Lecturer will give his final feedback on each excercise solution that students submit. The feedback and evaluation is voluntary and is not part of the grade.

The lectures contain small quizzes and assignments, where the answers are discussed together.

Detailed reviews of exam-relevant examples are presented at the lectures and uploaded on the course's website.
Student workload
Lectures 32 hours
Exercise classes 16 hours
Examination 30 hours
Preparation 128 hours
Expected literature

The main coursebook:
Fan, Li, Zhang, and Zou (2020). Statistical foundations of data science. Chapman and Hall/CRC.

 

Supplementary literature:

 

Predictive machine learning:

Hastie, Tibshirani og Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction. Anden udgave, Springer.

▶ online copy:   https:/​​/​​hastie.su.domains/​​ElemStatLearn/​​printings/​​ESLII_print12_toc.pdf

 

Causal machine learning:

Chernozhukov, V., Hansen, C., Kallus, N., Spindler, M., & Syrgkanis, V. (2024). Applied causal inference powered by ML and AI. arXiv preprint arXiv:2403.02467.

▶ online copy: https:/​/​causalml-book.org/​
Last updated on 26-01-2026