TDT4173 - Machine Learning


Examination arrangement

Examination arrangement: Portfolio
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio 100/100

Course content

The course introduces the principles and methods for automatic learning in computer systems. The main focus is data-driven learning with generalization performance measures on newly coming data, with its concepts, techniques, and analysis methods taught. Time series methods, ensemble methods, deep learning, probabilistic approaches, and reproducibility are also included. The strengths and weaknesses of various methods are discussed. Learning methods in case-based reasoning will be described as well. Besides theoretical knowledge, the course provides practical guidance for developing a machine learning software system.

Learning outcome

The course aims to introduce principles of machine learning methods and to give understanding of key elements in real-world machine learning applications.

Learning methods and activities

Lectures, group work, colloquia, and self-study.

Further on evaluation

The course evaluation includes two parts. (1) Every student must first pass an individual assignment (IA) about one month after the course begins. Each student can get a second attempt to pass the IA, but the student will receive a deduction (-5%) in the course points. Students who fail the IA for both attempts will receive an F or Fail course grade. (2) Only those who pass the IA can continue to the course project. The project is graded for the whole team (each team comprises up to three students). The project points equal base points (max. 100% and min. 41%) plus potential project deductions (from 0% to -17%). The base points are proportional to the number of Virtual Teams (VTs) defeated by the student team in terms of prediction performance. The teachers and teaching assistants prepare the VTs. If a student team cannot defeat any VT, the team members will fail the project and thus the course. The potential deductions include a late submission (within three days) and failure to document key components in machine learning practice.

The course points will then be rounded to a letter grade according to the NTNU standard ranges. If the student receives an F or Fail as a final grade, the student must retake the whole course.

Course materials


  • Tom Mitchell: Machine learning, McGraw Hill, 1997.
  • Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006

Selected papers.

Credit reductions

Course code Reduction From To
IT3704 7.5 AUTUMN 2008
MNFIT374 7.5 AUTUMN 2008
MNFIT374 7.5 AUTUMN 2008
IMT4133 5.0 AUTUMN 2023
More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  AUTUMN 2023

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Industrial Economics
  • Information Security
  • Informatics
  • Psychology
  • Statistics
  • Technological subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science


Examination arrangement: Portfolio

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Portfolio 100/100



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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