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2021/2022  KAN-COECV3009U  Introduction to Machine Learning for Economics

English Title
Introduction to Machine Learning for Economics

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for MSc in Advanced Economics and Finance
Course coordinator
  • Dolores Romero Morales - Department of Economics (ECON)
Main academic disciplines
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 27-01-2021

Relevant links
Learning objectives
  • Demonstrate awareness of how Machine Learning helps to extract and represent knowledge of complex data
  • Demonstrate knowledge of Supervised Learning theory in Decision Making, such as in Credit Scoring
  • Demonstrate knowledge of Unsupervised Learning theory in Decision Making, such as in Customer Segmentation
  • Demonstrate knowledge of Dimensionality Reduction theory for reporting final results
  • Be confident users of package computer programs that are widely used in industry for Supervised and Unsupervised Learning
  • Demonstrate awareness of how economists are integrating the tools of machine learning with econometric techniques in current empirical research.
Course prerequisites
1. Please note that this course is taught at an elite level. More specifically, students are required to have taken Econometrics in the first year of MSc in Advanced Economics and Finance, or an equivalent course..

2. Please send in a motivational letter (max. 200 words), arguing why you want to participate, and a 1 page graduate grade transcript. Send this to: ily.stu@cbs.dk before the registration deadline for elective courses. You may find the registration deadlines on my.cbs.dk ( https:/​/​studentcbs.sharepoint.com/​graduate/​pages/​registration-for-electives.aspx ).

Please also remember to sign up through the online registration.
Examination
Introduction to Machine Learning for Economics:
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 Written assignment
Duration 2 weeks to prepare
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

Two-week home assignment will be circulated to the students on the last day of the course
Course content, structure and pedagogical approach

In the current environment where data abounds of different complexity, it is crucial to extract and represent knowledge from business data. In Data Science, rational business decisions are made after harnessing different sources of data. Typical examples are credit scoring, bankruptcy prediction, fraud detection, customer loyalty, recommender systems, and revenue management. Building on theories of Supervised and Unsupervised Learning, this course aims to enhance your ability to apply Data Mining and Visualization tools for harnessing data. You will be exposed to the mathematical optimization models behind many of these tools, and the advantages that this mathematical modelling bring. The course uses computer software to illustrate how to apply the methodologies introduced.

 

The course’s development of personal competences:

 

During the course, and through a hands-on approach supported by Supervised and Unsupervised Learning theory, students will develop quantitative as well as mathematical modelling skills needed for Data Driven Decision Making. In addition, students will learn to appreciate the importance of using the right visualization tool to report final results.
Description of the teaching methods
Lectures, Demos, Computer Workshops
Feedback during the teaching period
Office hours and PC Workshops
Student workload
Preparation 96 hours
Classes 36 hours
Exam 72 hours
Expected literature

Athey, S. and G.W. Imbens (2019), Machine learning methods that economists should know about. Annual Review of Economics, 11:685–725.

 

Carrizosa, E., C. Molero-Río and D. Romero Morales (2021). Mathematical optimization in classification and regression trees. Technical Report, Copenhagen Business School, Denmark, https:/​/​www.researchgate.net/​publication/​346922645_Mathematical_optimization_in_classification_and_regression_trees.

 

Carrizosa, E. and D. Romero Morales (2013). Supervised classification and mathematical optimization. Computers and Operations Research, 40, 150-165.

 

Efron, B. (2020), Prediction, estimation, and attribution. Journal of the American Statistical Association, 115, 636-655.

 

T. Hastie, R. Tibshirani and J. Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer.

 

G. James, D. Witten, T. Hastie and R. Tibshirani (2016), An Introduction to Statistical Learning: with Applications in R. Springer.

 
Last updated on 27-01-2021