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)
7. semester
Duration of the subject/module
1 semester
Programme elements
Start time
Håndværkergården, København N
Subject number
Responsible for the subject(s)/modul(es)
Arturo Mora Rioja
Henrik Strøm
  • Purpose and learning objectives

    The objective of the module is to qualify the student to work with Artificial Intelligence systems and to apply data science methods in a structured manner, extract inferential knowledge from a dataset, and make probabilistic forecasts using artificial intelligence and machine learning models. In addition, the student must be able to report on the findings and make use of visualization.


    • various definitions of the fields of Artificial Intelligence, Machine Learning, and Data Science
    • what artificial intelligence and machine learning really is
    • what is possible and not possible using artificial intelligence and machine learning
    • designing experiments for data collection
    • using a framework for numerical computations
    • using a framework for general data analysis
    • using a framework for descriptive and inferential statistical analysis
    • using a framework for probabilistic forecasting using machine learning models
    • a variety of data analysis algorithms and their applications


    • how to ask a relevant and interesting research question (framing the problem)
    • collect and obtain data from a variety of sources
    • organize data to prepare for analysis
    • explore data to gain insights
    • apply basic descriptive and inferential statistics
    • make forecasts using probabilistic machine learning tools
    • use methods to facilitate reproducibility and transparency
    • communicate findings in a written report, using visualizations


    The objective is that the student will have acquired proficiency in participating and contributing in projects using Artificial Intelligence, Machine Learning, and Data Science.

  • 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.
  • 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
    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)
    Type of evaluation
    7-point grading scale
    Internal censure
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 60 hours of instruction, which corresponds to 80 lessons (1 lesson = 45 min.) and 22% of your total workload for the subject.

The teaching primarily consists of the following activities: classroom teaching, project work, peer-review, group work.
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