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Course syllabus - Learning Systems

Scope

7.5 credits

Course code

DVA493

Valid from

Autumn semester 2026

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only first-cycle course/s as entry requirements)

Main area(s)

Computer Science

Organisation

Department of Computer Science & Engineering

Ratified

2022-01-24

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

    Mitchell, Tom M.

    Machine learning

    ISBN: 0-07-042807-7

    LIBRIS-ID: 8273683

    New York, McGraw-Hill, xvii, 414 s.

    Mandatory

Objectives

This course aims to give a wide knowledge in different learning systems, advantages, limits and application areas.

Learning outcomes

After completing the course the student should be able to:

  1. understand machine learning algorithms,
  2. formulate a problem, apply an algorithm and explain why it is suitable for the given problem as well as show how it can solve the problem and also
  3. analyze the results of applying an algorithm to a problem, and discuss the advantages and limitations of the algorithm.

Course content

Linear regression, logistic regression, decision trees, Bayesian learning, metaheuristic optimization (genetic algorithm, differential evolution), K-means clustering, principal component analysis, artificial neural networks, reinforcement learning (Q-learning).

Specific requirements

90 credits of completed courses including Programming 7.5 credits and Programming for Embedded Systems 7.5 credits or Data Structures, Algorithms and Program Development 7.5 credits. In addition Swedish course 3 or Swedish level 3 and English course 6 or English level 2 are required. For courses given entirely in English exemption is made from the requirement in Swedish course 3 or Swedish level 3.

Examination

Written examination (TEN1), 3.5 credits, examines the learning outcomes 1 and 2, marks Fail (U), 3, 4 or 5.

Laboratory work (LAB1), an assignment that is presented with a report and a demonstration to the teacher, 4 credits, examines the learning objectives 1, 2 and 3, 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

Grading scale: 5, 4, 3

Interim Regulations and Other Regulations

The course completely overlaps with CDT407/DVA427 Learning Systems and DVA308 Maskininlärning.