The course is included in these curricula and study modules
- Information technology 2014 - Machine learning and decision support system development
- Information technology 2015 - Machine learning and decision support system development
- Information technology 2017 - Machine learning and decision support system development
- Information technology 2018 - Machine learning and decision support system development
- Information technology 2019 - Machine learning and decision support system development
- Information technology 2020 - Machine learning and decision support system development
- Information technology 2021 - Machine learning and decision support system development - advanced studies
- Information technology 2022 - Machine learning and decision support system development - advanced studies
- Information technology 2023 - Machine learning and decision support system development - advanced studies
The course takes place in period
4 (2024-03-18 to 2024-07-31)
Level/category
Teaching language
English
Type of course
Compulsory
Cycle/level of course
First
Recommended year of study
3
Total number of ECTS
5 cr
Competency aims
Within this study unit we will focus on the
following competences:
Machine learning and decision support system
development with an emphasis on:
Time series forecasting
SDG's in focus:
#4: Quality education
#9: Industry, innovation and infrastructure
Learning outcomes
After completion of the study unit:
You know basic analytical concepts. (Knowledge)
You can interpret and process input and output
data. (Skills)
You can manage and analyse time series and perform
regression analysis. (Skills)
You are able to solve specific problems where
supervised learning is used as a training method
to create models that predict future events.
(Skills)
Prerequisites and co-requisites
Statistics and probability
Data processing
Design of analytical systems
Computer vision
Recommended or required reading
Material is provided through Itslearning.
Study activities
- Lectures - 30 hours
- Individual- and group instruction - 30 hours
- Project- and production work/artistic activities - 60 hours
- Individual studies - 15 hours
Workload
- Total workload of the course: 135 hours
- Of which autonomous studies: 135 hours
- Of which scheduled studies: 0 hours
Mode of Delivery
Participation in tuition
Assessment methods
- Essays, reports, productions and portfolio
- Project work and presentation
Teacher
Dayama Niraj
Examiner
Dayama Niraj
Home page of the course
Group size
No limit
Assignments valid until
12 months after course has ended
Course enrolment period
2023-11-24 to 2023-12-22
Assessment methods
- Date will be announced later - Reports and productions
- 2022-03-28 - Other assignments