Course code VU-INN280F

VU-INN280F Business analytics III: Introduction to machine learning

There may be changes to the course due to to corona restrictions. See Canvas and StudentWeb for info.

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Showing course contents for the educational year 2021 - 2022 .

Course responsible: Joachim Scholderer
Teachers: Therese Seljevold
ECTS credits: 2
Faculty: Senter for etter- og videreutdanning
Teaching language: NO
(NO=norsk, EN=Engelsk)
Limits of class size:
.  
Teaching exam periods:
This course starts in the Autumn parallel. This course has teaching/evaluation in the Autumn parallel.
Course frequency: Annually
First time: 2020H
Preferential right:
.
Course contents:

The topic of this online course is how to use basic machine learning techniques in the company's daily business:

  • Where in your company can you use machine learning,
  • How to ensure that you have the right data,
  • How to use machine learning techniques for anomaly detection, customer segmentation and sales forecasting,
  • How to leverage the results to improve business decisions.

This course is targeted at private and public companies that want to get started with their digitalisation initiatives, employees who want to upgrade their qualifications, and people currently without employment who want to acquire future-oriented abilities and skills.

Learning outcome:

Knowledge

  • Know basic machine learning techniques
  • Understand their assumptions and data requirements
  • Have an overview of how machine learning models can be used to streamline business processes

Skills

  • Be able to perform basic analyses with the help of machine learning techniques

General competence

  • Be able to work cross-functionally in business management and IT
  • Be able to contribute constructively in digitalisation and process improvement projects
Learning activities:
Video lectures, exercises with data and software, independent work on two project assignments
Teaching support:
Canvas and project supervision
Syllabus:
Olsen, K. A. (2019). God digitalisering. Oslo: Cappelen Damm.Selected journal articles and book chapters
Prerequisites:
Basic knowledge of statistics and data analysis
Recommended prerequisites:
Basic programming skills
Assessment:
Portfolio assessment, consisting of two project assignments (weight: 50% each): pass/fail
Nominal workload:
50 hours
Entrance requirements:
Minimum requirements for entrance to higher education in Norway (generell studiekompetanse)
Reduction of credits:
DAT200 (2 ECTS), INN355 (2 ECTS)
Type of course:
  • Video lectures: 5 hours
  • Exercises with data and software: 5 hours
  • Project assignments: 25 hours
  • Self-study/syllabus literature: 15 hours
Examiner:
External examiner will control the quality of the syllabus, questions for the examination, and principles for the assessment of the examination answers
Examination details: Portfolio: Passed / Failed