Course code INN355

INN355 Machine Learning for Business Process Optimisation

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Showing course contents for the educational year starting in 2020 .

Course responsible: Joachim Scholderer
Teachers: Mike Riess
ECTS credits: 10
Faculty: School of Economics and Business
Teaching language: EN
(NO=norsk, EN=Engelsk)
Teaching exam periods:
This course starts in the Spring parallel. This course has teaching/evaluation in the Spring parallel.
Course frequency: Annual
First time: 2017H
Last time: 2021H
Course contents:

Digitalisation and process automation are the hallmark of Industry 4.0. Machine learning plays a prominent role in this, providing the algorithmic and statistical "toolbox" for the automation and data-driven optimisation of business processes. The course has a two aims. The first aim is to introduce participants to the machine learning methodologies and data science tools that are central in business applications:

  • Data visualisation and reporting (dimension reduction, plotting techniques, dashboard design),
  • Predictive analytics (regularised least squares techniques, nearest neighbour, decision trees, ensemble methods such as bagging and boosting),
  • Process analysis (time-to-event modelling, anomaly detection, process mining)
  • Model assessment and selection (bias-variance trade-off, partitioning and cross-validation, decision theory, cost-sensitive learning).

The biggest risk in applied machine learning projects is a disconnect between the orderly world of data science and the messy reality of business processes. Hence, the second aim is to cultivate participants' ability to manage machine-learning projects in real business contexts:    

  • Requirements engineering (strategic and operational context, collaboration with project stakeholders, elicitation and specification of requirements, life cycle management),
  • Data management (enterprise architecture, data protection and security, data management plans, data quality, feature engineering),
  • Model deployment (implementation, performance management, error handling, updating).

Hands-on work on real and current cases is a key part of the course. Participants will work in teams    on a semester-long case project, organised in close cooperation with local and international international businesses.

Learning outcome:


  • Understand the capabilities of important machine learning techniques,
  • Know how to leverage these techniques to automate decisions and optimise business processes,
  • Know how to train, tune and test models for classification, prediction and anomaly detection,
  • Be able to choose the best model from among several competing models.


  • Be able to use high-level platforms such as SAS Enterprise Miner and Celonis to build models for classification, prediction and anomaly detection,
  • Know how to deploy machine learning models in typical business process flows.

General competence:

  • Be able to manage process automation projects in a wide range of business contexts,
  • Understand the need to align model deployment with data management and enterprise architecture,
  • Be able to work in cross-functional project structures,
  • Understand and manage ethical and regulatory issues in digital business contexts.
Learning activities:
Lectures, workshops and tutorials under supervision, flipped classroom activities, assignments and independent teamwork related to the case project.

Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning: Data mining, inference and prediction (2nd Ed.). Berlin: Springer. 

Tutz, G., & Schmid, M. (2016). Modeling discrete time-to-event data. Cham: Springer.

Van der Aalst, W. (2016). Process mining: Data science in action (2nd Ed.). Berlin: Springer.

In addition, the readings will include a selection of journal articles and case studies. The more tool-oriented parts of the course programme will be supported by online tutorials. Details will be announced on the Canvas page of the course at the beginning of the semester.

MATH100 Introductory mathematics or ECN102 Introduction to mathematics for economists; STAT100 Statistics; BUS350 Introduction to data analytics or INF230 Data processing and analysis; STAT200 Regression analysis or ECN201 Econometrics or BUS326 Applied financial econometrics; INN350 Digitalisation and control of business processes
Recommended prerequisites:
INF120 Programming and data processing; MATH131 Linear Algebra or ECN302 Mathematics for Economists; BUS230 Operations research; BUS240 Operations management or IND210 Industrial management
Mandatory activity:
Continuous exam, consisting of one project assignment conducted in groups of four participants (weight: 60%) and two individual multiple-choice tests (weight: 20% each). No re-sit examination will be arranged in this course.
Nominal workload:
300 hours. This is a very work-intensive course.
Entrance requirements:
3rd year (bachelor) or higher
Type of course:
Four lecture hours per week (February-May). In addition, intensive case work in project teams.
The course will be taught in English. Incoming students can contact student advisors at the School of Economics and Business ( for admission to the course. 
External examiner will control the quality of syllabus, questions for the final examination, and principles for the assessment of the examination answers.
Examination details: Continuous exam: A - E / F