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
The course takes place in period
4 (2020-03-23 to 2020-07-31)
Level/category
Teaching language
Swedish
Type of course
Compulsory
Cycle/level of course
First
Recommended year of study
3
Total number of ECTS
5 cr
Competency aims
The aim of the course is that the student
learns
to apply machine learning as a method for
processing data. The course focuses on the use
and treatment of time series data and then
perform regression analysis.
Learning outcomes
After completing the course, the student is
expected to be able to solve specific problems
where supervised learning is used as a training
method to create models that predict future
events.
Course contents
Analytical concepts
Interpretation and processing of input and output
Application of machine learning
Graphical representation of results
Prerequisites and co-requisites
The courses:
Data Processing and Data Science
Descriptive Analytics - Data/Text Mining
Computer vision
Additional information
The student should follow the announced time
frames
for the project, only in exceptional cases can
they
be changed.
Recommended or required reading
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
Assessment requirements
To pass the course the student is required to
complete and present a project announced through
ItsLearning.
Teacher
- Karlsson Jonny
- Scherbakov-Parland Andrej
- Westerlund Magnus
- Shamsuzzaman Md
Examiner
Westerlund Magnus
Home page of the course
Group size
No limit (33 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2020-03-09 to 2020-04-05
Assessment methods
Date will be announced later - Reports and productions
Date | Time | Room | Title | Description | Organizer |
---|---|---|---|---|---|
2020-03-25 | 10:15 - 13:30 | F365 | Prediktiv analytik | Westerlund Magnus | |
2020-03-26 | 10:15 - 12:15 | B323 | Prediktiv analytik | Westerlund Magnus | |
2020-03-30 | 10:15 - 13:00 | F365 | Prediktiv analytik | Westerlund Magnus | |
2020-04-07 | 12:00 - 14:00 | B323 | Prediktiv analytik | Westerlund Magnus | |
2020-04-09 | 09:15 - 12:15 | B323 | Prediktiv analytik | Md | Westerlund Magnus |
2020-04-16 | 09:15 - 12:15 | B323 | Prediktiv analytik | Md | Westerlund Magnus |
2020-04-23 | 09:15 - 12:15 | B323 | Prediktiv analytik | Md | Westerlund Magnus |
2020-04-28 | 14:15 - 16:00 | Prediktiv analytik | Coding session | Shamsuzzaman Md Westerlund Magnus |
|
2020-04-30 | 14:15 - 16:00 | Prediktiv analytik | Coding session | Shamsuzzaman Md Westerlund Magnus |
|
2020-05-04 | 14:15 - 16:00 | Prediktiv analytik | Coding session | Shamsuzzaman Md Westerlund Magnus |
|
2020-05-05 | 12:15 - 15:00 | B323 | Prediktiv analytik | Projektpresentation | Scherbakov-Parland Andrej Westerlund Magnus |