Copenhagen School of Design and Technology


Applied Artificial Intelligence

Danish title
Applied Artificial Intelligence
Study programme
Software Development
Type of education
Full time education
Level of education
Bachelor (top-up)
Duration of the subject/module
1 semester
Programme elements
Start time
Håndværkergården, København N
Subject number
Responsible for the subject(s)/modul(es)
Henrik Strøm
Arturo Mora Rioja
  • Purpose and learning objectives

    The purpose of this elective is for the student to develop practical skills in applying
    artificial intelligence, machine learning, and data science, to solve problems that can
    not be solved using traditional software development methods


    The student will gain knowledge on some of the most prominent areas of artificial intelligence:
    • deep learning
    • supervised learning: classification and regression
    • unsupervised learning: cluster analysis, anomaly detection, prototype/archetype analysis
    • reinforcement learning: Q-learning and Deep Q-learning
    • appropriate use of performance metrics

    The student will understand and be able to reflect on questions like:
    • what is artificial intelligence
    • what kind of problems can be solved with artificial intelligence, and which models should be applied


    The student will gain skills to:
    • solve practical problems using artificial intelligence and machine learning models
    • identify and implement the most applicable models
    • prepare data for use with machine learning models
    • optimize models and solve common issues such as overfitting and underfitting
    • apply performance metrics for model analysis
    • communicate findings according to scientific standards


    The student will learn to:
    • develop solutions based on artificial intelligence that solves problems that can not be solved using traditional software development methods
    • apply the scientific method to find solutions systematically
    • formulate research questions, hypotheses, and null-hypotheses
    • find and apply scientific articles for learning and to support own findings

  • Type of instruction
    The course is based on Problem-Based Learning. The students must apply knowledge from the classroom sessions in their project work, and acquire knowledge on their own to complete the projects. The teacher works in a supervisor role during project work, so the students are never left on their own.
  • Subject/module requirement for participation

    Equipment needed to participate
    Windows PC, Mac or Linux
    - min 8 GB RAM (16 GB RAM preferably)
    - 200 GB available disk space
    - CPU with virtualisation hardware (usually available in most modern CPUs)
    - 15,6” screen or bigger is recommended

  • Exam

    The learning outcomes of the exam are identical with the learning outcomes of the subject(s)/modul(es)

    Prerequisites for access to the examination
    Mandatory Assignment 1 and 2 approved.
    Exam in one or more subjects
    Subject/module is tested standalone
    10 min individual presentation of the project, 15 min. examination in project and course curriculum.
    Type of exam
    Combined written and oral examination
    Individual exam or group exam
    Exam languages
    Danish (Norwegian/Swedish)
    Presentation - max 10 min.
    Examination- max. 15 min.
    Grading - 5 min.
    Total - 30 min.
    Permitted exam aids
    Available exam aids
    Type of evaluation
    7-point grading scale
    Internal censure
  • Preliminary literature list
    This is a preliminary literature list. A final literature list will be provided in connection with study start.
    Course materials available to the students via Internet.
  • Additional information
    This elective subject may not run in certain semesters if not enough students choose it.

    Some classes may take place online.
hours of teaching
hours of preparation
The figure shows the extent of workload related to the subject divided into different study activities.

In the subject Applied Artificial Intelligence you will receive 80 hours of instruction, which corresponds to 106 lessons (1 lesson = 45 min.) and 29% of your total workload for the subject.

The teaching primarily consists of the following activities: classroom teaching, project work, peer-review, group work, Online teaching.
The preparation primarily consists of the following activities: project work, searching for information, reading your own notes, reading the curriculum.

Read about KEAs Study Activity Model

*KEA can deviate from the number of hours if this is justified by special circumstances