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Course syllabus - Introduction to Applied AI

Scope

7.5 credits

Course code

DVA142

Valid from

Autumn semester 2026

Education level

First cycle

Progressive Specialisation

G1N (First cycle, has only upper-secondary level entry requirements)

Main area(s)

Computer Science

Organisation

Department of Engineering Sciences

Ratified

2025-01-16

Revised

2025-11-03

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2025-05-21
    • Latest update: 2025-05-27

    Books

    Russell, Stuart J.; Norvig, Peter

    Artificial intelligence: a modern approach

    ISBN: 1292401133

    LIBRIS-ID: p3vvfhm1m3dsszsg

    Fourth edition global edition, Harlow, Pearson Education Limited, 1166 pages

    Mandatory

Objectives

The purpose of this course is to introduce the students to the topic of Artificial Intelligence (AI), including possible and future applications. In addition, the history of the AI, its impact on society including aspects regarding ethics, gender and equality, will be covered.

Learning outcomes

After completing the course, the student should be able to:

  1. describe the subject area of AI, its history, and its development as a scientific and popular scientific topic,
  2. describe the role of AI in the society, including relevant aspects regarding ethics, gender and equality,
  3. demonstrate the ability to differentiate the different concepts and parts of an AI software such as the problem definition, the data collection, the AI algorithm and the presentation of the results,
  4. write a technical report focusing on AI also including discussion around ethics or gender/equality and also
  5. demonstrate the ability to solve problems in AI.

Course content

General orientation of the subject AI, and design and use of AI algorithms. The course introduces also to central activities in the programme, such as report writing and group work, as well as to the role as engineer, in a societal context, and as a professional AI-expert.

Requirements

Basic eligibility and Mathematics 3b or 3c or Mathematics C Or: Mathematics – Further level 1b or Mathematics – Further level 1c

Examination

Compulsory attendance (OBN1), 1 credit, examines the learning objectives 1-2, marks Fail (U) or Pass (G).

Written assignment (INL1), 2 credits, examines the learning objectives 3-4, marks Fail (U) or Pass (G).

Laboratory work 1 (LAB1), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credit, examines the learning objective 5, marks Fail (U) or Pass (G).

Laboratory work 2 (LAB2), an assignment that is presented with a report and a demonstration to the teacher, 1 credit, examines the learning objective 5, marks Fail (U) or Pass (G).

Laboratory work 3 (LAB3), an assignment that is presented with a report and a demonstration to the teacher, 1 credit, examines the learning objective 5, marks Fail (U) or Pass (G).

Laboratory work 4 (LAB4), an assignment that is presented with a report and a demonstration to the teacher, 1 credit, examines the learning objective 5,
marks Fail (U) or Pass (G).

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Two-grade scale

Interim Regulations and Other Regulations

The course completely overlaps with DVA132/DVA136 Introduction to Applied AI.