IDATT2502 - Applied machine learning with project


Examination arrangement

Examination arrangement: Project
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Project 100/100

Course content

Data representation: representation of various data sources such as images, sound and text, current techniques for processing data.

Unsupervised learning: various clustering algorithms, reduction of dimensions, and other current methods.

Supervised learning: including logistic regression and different types of neural networks.

Learning outcome


The candidate can give an account of:

  • different ways of representing data
  • different methods for grouping and classifying data
  • which machine learning methods are appropriate to use for given problems
  • limitations of machine learning


The candidate can:

  • create full-fledged machine learning solutions using a framework
  • use representation algorithms that make it easier for machine learning methods to give better results for a given data set
  • select and adapt a machine learning method that is relevant to a given problem
  • assess whether machine learning methods can give good results for a given problem based on a given data set

General competence

The candidate must be able to find and adapt solutions to new problems based on previous applications of machine learning.

Learning methods and activities

Lectures, exercises and project.

Compulsory assignments

  • Mandatory exercises

Further on evaluation

Work requirements: Mandatory exercises are given, all of which must be approved.

New/re-sit examination: next time the course is run.

Specific conditions

Course materials

Programming examples, presentations and operating instructions with auxiliary literature through external resources.

Credit reductions

Course code Reduction From To
TDAT3025 7.5 AUTUMN 2021
More on the course



Version: 1
Credits:  7.5 SP
Study level: Third-year courses, level III


Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
  • Engineering
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science


Examination arrangement: Project

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Project 100/100 INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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