The course is included in these curricula and study modules

The course takes place in period

3 (2024-01-01 to 2024-03-17)

Level/category

Professional studies

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

Course and curriculum search