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
3 (2024-01-01 to 2024-03-17)
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
In this study unity we will focus on the following
competences:
Machine learning, decision support system
developemtn and artificial intelligence with focus
on:
Deep learning
Object detection and localization
SDG's in focus:
#4: Quality education
#9: Industry, innovation and infrastructure
Learning outcomes
After completed study unit:
You know how a digital image is structured and how
different filters can be applied to modify or
enhance specific features (knowledge)
You know how commont algorithms for face detection
and recognition works (knowledge)
You understand the basics of how neural and
convolutional neural networks work (knowledge)
You can apply algorithms for face detection and
recognition in programming code (skill)
You can design and train deep learning models for
image classification and object detection (skill)
You see the potential of computer vision
applications and how they can be utilized in
various areas (approach)
Course contents
Introduction to computervision and the OpenCV
Library
Pyhon and OpenCV in Anaconda
Image processing basics
Pxel relations
Statistics and Histograms
Histogram equalization
Filtering
Face detection and recognition
Introduction to neural networks
Deep learning for image clasification and object
detection
Prerequisites and co-requisites
Data Processing
Study activities
- Lectures - 40 hours
- Project- and production work/artistic activities - 95 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
QUIZ tests and
programming project
Assessment requirements
The course grade is assessed based on 4 smaller QUIZ
tests (weight 20%) and a programming project (weight
80%). A minimum of 50% of the total points is
required for passed course.
Teacher
Karlsson Jonny
Examiner
Karlsson Jonny
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 - Other assignments