Course code INN355

INN355 Machine Learning for Business Process Optimisation

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Showing course contents for the educational year starting in 2017 .

Course responsible: Joachim Scholderer
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
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),
  • Pattern discovery (clustering, outlier detection, association rules, sequence and time series mining),
  • Predictive analytics (regularised least squares techniques, nearest neighbour, decision trees, neural networks, support vector machines, ensemble methods),
  • 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 business cases is a key part of the course. Participants will work in teams on two case challenges, organised in close cooperation with local and international businesses.

Learning outcome:

Knowledge:

  • 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
  • Know how to deploy machine learning models in typical business process flows

Skills:

  • Be able to use data visualisation tools such as Tableau and self-service analytics tools such as Power BI to develop dashboards and reporting systems
  • Be able to use high-level platforms such as SAS Enterprise Miner to build models for classification, prediction and anomaly detection

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 challenges.
Syllabus:

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The elements of statistical learning: Data mining, inference and prediction (2nd Ed.). Berlin: Springer. (E-book download and companion website: http://www-stat.stanford.edu/~tibs/ElemStatLearn/)

Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning (2nd Ed.). Boston, MA: Harvard Business School Publishing.

Valacich, J. S., & George, J. F. (2015). Modern systems analysis and design (8th Ed.). London: Pearson.

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.

Prerequisites:
STAT100 Statistics
Recommended prerequisites:

MATH100 Introductory Mathematics

STAT200 Regression Analysis or ECN201 Econometrics

Mandatory activity:
-
Assessment:
Two project assignments (weight: 50% each), conducted in groups of four participants, related to the case challenges. No re-sit examination will be arranged.
Nominal workload:
300 hours
Entrance requirements:
Bachelor degree or equivalent
Reduction of credits:
DAT200 Applied machine learning (credit reduction: 5 ECTS)
Type of course:
Lectures: 40 hours. Case workshops and guidance: 16 hours.
Examiner:
Examination details: Continuous exam: A - E / Ikke bestått