IA-2-024 (0) - Machine Learning and Optimization

• 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

2 (2022-10-24 to 2022-12-31)

Swedish

Compulsory

First

4

5 cr

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

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 (skill)

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.

The assignments and the course work are given
during the course. The presentation of the course
work is done during period 2.

Are given in the lectures and on itslearning

• Lectures - 28 hours
• Project- and production work/artistic activities - 40 hours
• Individual studies - 67 hours

• Total workload of the course: 135 hours
• Of which autonomous studies: 135 hours
• Of which scheduled studies: 0 hours

Participation in tuition

Essays, reports, productions and portfolio

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.

Björk Kaj-Mikael

Björk Kaj-Mikael

https://arcada.itslearning.com/ External link

No limit (31 students enrolled)

12 months after course has ended

2022-10-10 to 2022-11-06

Date will be announced later - Reports and productions