2025/2026 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
|
Course coordinator | |
|
|
Main academic disciplines | |
|
|
Teaching methods | |
|
|
Last updated on 19-05-2025 |
Relevant links |
Learning objectives | ||||||||||||||||||||||
At the end of the course, students are expected
to be able to
|
||||||||||||||||||||||
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 | ||||||||||||||||||||||
|
||||||||||||||||||||||
Course content, structure and pedagogical approach | ||||||||||||||||||||||
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:
|
||||||||||||||||||||||
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
Research-like activities
|
||||||||||||||||||||||
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 | ||||||||||||||||||||||
|
||||||||||||||||||||||
Expected literature | ||||||||||||||||||||||
The main coursebook:
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/ |