Course code INN358

INN358 Machine Learning with Discrete Event Stream Data

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Showing course contents for the educational year 2021 - 2022 .

Course responsible: Joachim Scholderer
Teachers: Mike Riess
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: EN
(NO=norsk, EN=Engelsk)
Limits of class size:
Teaching exam periods:
This course will be offered first time  autumn 2022. This course starts in the Autumn parallel. This course has teaching/evaluation in the Autumn parallel.
Course frequency: Annually
First time: Study year 2021-2022
Preferential right:
  • Study specialisations where this course is mandatory have priority: digital business transformation (M-EI), business analytics (M-TDV) and business analytics (M-ØA)
  • Students from data science (M-DV and M-TDV), entrepreneurship and innovation (M-EI), industrial economics and technology management (M-IØ) and business administration (M-ØA) who have passed all mandatory and recommended prerequisites for the course
Course contents:

Most standard techniques in machine learning are designed for cross-sectional data. Discrete event stream data (e,.g., event log data from ERP, CRM and SCM systems) also have a time dimension, which leads to complex dependency structures in the data. In this course, participants will learn advanced modelling and prediction techniques that can handle such data.

  • Dependency structures in discrete event stream data
  • Pre-processing and feature engineering
  • Use of standard machine learning techniques with aggregated event stream data
  • Process mining, association rules and link analysis
  • Time-to-event models
  • Hidden Markov models
  • Dynamic Bayesian networks
  • Model assessment and selection
  • Implementation in Python, SAS and R

Practical work with real cases is an important part of the course. Participants will work in groups on a semester-long case project.

Learning outcome:


  • Understand the dependency structures in discrete event stream data
  • Understand the theoretical foundations of modelling and prediction techniques that can handle discrete event stream data


  • Be able to perform descriptive analyses of discrete event stream data
  • Be able to develop prediction models based on discrete event stream data
  • Be able to evaluate models and choose between competing models

General competence

  • Be able to work in cross-functional project structures
  • Be able to contribute constructively in process automation projects
Learning activities:
Lectures, exercises with data and software, case workshops under supervision, independent group work related to the project assignment
Teaching support:
Canvas, Microsoft Teams
Tutz, G., & Schmid, M. (2016). Modeling discrete time-to-event data. Cham: Springer.Selected journal articles and book chapters
DAT200 Applied machine learningINF230 Data processing and analysis

Recommended prerequisites:
  • INN265 Analysis of business processes
Portfolio assessment , consisting of an individual midway home exam (weight. 50%) and a project assignment conducted in groups of four participants (weight: 50%). No re-sit examination will be arranged in this course.
Nominal workload:
125 hours
Reduction of credits:
INN355 (5 ECTS)
Type of course:
  • Lectures and case workshops: 20 hours
  • Exercises with data and software: 5 hours
  • Project assignment: 50 hours
  • Self-study/syllabus literature: 50 hours
External examiner will control the quality of the syllabus, questions for the examination, and principles for the assessment of the examination answers.
Examination details: :