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 2016 - 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
2 (2023-10-23 to 2023-12-31)
Level/category
Teaching language
Swedish
Type of course
Compulsory
Cycle/level of course
First
Recommended year of study
4
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:
Process optimization
Application of different methods to optimize a
process
Application of optimization in machine learning
SDGs in focus:
1 NO POVERTY
4 QUALITY EDUCATION
8 DECENT WORK AND ECONOMIC GROWTH
9 INDUSTRY, INNOVATION AND INFRASTRUCTURE
Learning outcomes
Upon completion of the study unit:
You have an understanding of what optimization is
and how it can be used in machine learning.
(Knowledge)
You have an in-depth knowledge of methods that can
be used to optimise a process. (Knowledge)
You can solve different optimization problems in
practice. (Skills)
Course contents
The course is done through submitting a number
of assignments and we will go through the
material during the lessons. After the course
the student should be able to understand how a
process can be optimized, and understand also
how linear programming and convex problems are
formulated. The course is examined through the
assignments and the course project work.
Additional information
The assignments and the course work are given
during the course. The presentation of the course
work is done during period 2.
Recommended or required reading
Are given in the lectures and on itslearning
Study activities
- Lectures - 28 hours
- Project- and production work/artistic activities - 40 hours
- Individual studies - 67 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 should pass the
following examinations:
Examination 1 Assignments
Examination 2 Course work
Students can get 60 % of the grade from the
assignments and 40 % from the course work.
Teacher
Dayama Niraj
Examiner
Dayama Niraj
Home page of the course
Group size
No limit (25 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2023-10-09 to 2023-11-05
Assessment methods
Date will be announced later - Reports and productions
Date | Time | Room | Title | Description | Organizer |
---|---|---|---|---|---|
2023-11-15 | 13:00 - 15:00 | E387 | Maskininlärning och optimering | Dayama Niraj | |
2023-11-17 | 13:00 - 15:00 | A409 | Maskininlärning och optimering | Dayama Niraj | |
2023-11-20 | 09:15 - 11:00 | F143 | Sisusession för IT & Media åk 1 och 2 & MTH1 på Campus och ONLINE | Detta är en hybridsession där ni lär er använda Sisu, så man kan delta endera på campus eller online. Om man deltar på campus rekommenderas att man tar egen dator med sig (en padda eller smarttelefon fungerar tyvärr inte). Sessionen ordnas av Arcadas studieärenden: sisu@arcada.fi DELTA VIA DENNA LÄNK: https://teams.microsoft.com/l/meetup-join/19%3ameeting_OTI1NGMzNzUtNjQwYy00MmE1LWIwYTktZmY1YTZjYjkzZTE2%40thread.v2/0?context=%7b%22Tid%22%3a%2286080f64-b23d-4f93-8c43-e65f2588b9c3%22%2c%22Oid%22%3a%2295120ab9-e6c5-4d68-b283-421bbe101856%22%7d Välkomna! | Ahlroth Siri Eerola Sabina |
2023-11-21 | 13:00 - 15:30 | E385 | Maskininlärning och optimering | Dayama Niraj | |
2023-11-27 | 10:00 - 12:00 | Z022 | Maskininlärning och optimering | MS Teams | Dayama Niraj |
2023-11-29 | 10:15 - 12:45 | F365 | Maskininlärning och optimering | Dayama Niraj | |
2023-11-30 | 13:00 - 15:30 | E385 | Maskininlärning och optimering | Dayama Niraj | |
2023-12-04 | 13:00 - 15:30 | E383 | Maskininlärning och optimering | Dayama Niraj | |
2023-12-05 | 13:00 - 15:30 | F365 | Maskininlärning och optimering | Dayama Niraj | |
2023-12-12 | 10:00 - 12:00 | F365 | Maskininlärning och optimering | Dayama Niraj | |
2023-12-13 | 09:30 - 12:00 | F365 | Maskininlärning och optimering | Dayama Niraj | |
2023-12-18 | 10:15 - 12:45 | E385 | Maskininlärning och optimering | Dayama Niraj | |
2023-12-19 | 10:15 - 12:45 | E387 | Maskininlärning och optimering | Dayama Niraj | |
2023-12-20 | 13:00 - 15:30 | F365 | Maskininlärning och optimering | Dayama Niraj |