IA-2-023

• 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

1 (2021-08-01 to 2021-10-24)

Swedish

Compulsory

First

4

5 cr

The aim of this course is to provide the students
with knowledge about thoughtful, responsible,
and ethical uses of machine learning practice,
which is a fundamental precondition to
trustworthy development of AI. Recommendation
engines, which simultaneously shape and predict
the future in nearly all parts of the human life,
will be the main use case during the course.

At the end of the course the student is expected
to have an understanding of the techniques
behind automated decision making systems related
to common ML practice using Python,
combined with the skill to judge if the decision
seems reasonable or not. The students will have a
detailed and holistic knowledge about the
concerns and issues regarding algorithmic
decision
making and how to address them.

- Limitations of AI
- Algorithmic Bias
- Recommender systems

IA-2-020 - Predictive analytics

?Real World AI: A Practical Guide for Responsible
Machine Learning? Alyssa Simpson
Rochwerger, Wilson Pang, Lioncrest Publishing,
March 5, 2021

?Recommendation Engines?, Michael Schrage, MIT
Press Essential Knowledge, September 1,
2020

• Lectures - 28 hours
• Practical exercises - 30 hours
• Project- and production work/artistic activities - 50 hours
• Individual studies - 27 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

Exam: 35p
At the end of the course there will be an exam
about the content learnt.

Final Project: 65p
To pass the course the student should, during the
project work, show why and how automated decision
making is designed and which potential issues and
challenges may come with it. The use case will be
a recommender system.

Extra points: 50p/nr. of groups
Presenting own decision tree example results using
normal and biased dataset.

GRADING:
94-100p = 5
88-93p = 4
82-87p = 3
76-81p = 2
70-75p = 1

• Becker Sandra
• Karlsson Jonny
• Scherbakov-Parland Andrej

Karlsson Jonny

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

No limit (43 students enrolled)

12 months after course has ended

2021-08-09 to 2021-09-05

Date will be announced later - Reports and productions