TDT4173 - Modern Machine Learning in Practice


Examination arrangement

Examination arrangement: Portfolio
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio 100/100

Course content

This course provides more comprehensive content on machine learning (ML) principles and techniques for developing practical ML systems. Topics covered include essential ML basics, modern prediction models, with a focus on ensemble learning, and critical steps in the ML pipeline, such as data preparation, manipulation, exploratory data analysis, data cleaning, feature engineering, and model interpretation. The course also addresses model evaluation, reproducibility, automatic ML, and specialized methods for time series data.

Learning outcome

By the end of the course, students will be prepared to develop robust machine-learning systems for real-world applications.

Learning methods and activities

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

Further on evaluation

The course evaluation consists of two components:

  1. Individual Assignment (IA): Approximately one month after the course commences, every student is required to complete an individual assignment (IA). Each student has the opportunity for a second attempt to pass the IA; however, in the event of a second attempt, a 5% deduction in course points will be applied. Students who do not pass the IA in both attempts will receive a final course grade of 'F' or 'Fail.'
  2. Course Project: Only those students who successfully pass the IA are eligible to proceed to the course project. Course projects are graded as a team effort, with each team comprising a maximum of three students. Project grading is determined by combining base points (ranging from a maximum of 100% to a minimum of 41%) with potential project deductions (ranging from 0% to -17%). Base points are proportional to the number of Virtual Teams (VTs) that the student team outperforms in terms of prediction performance. VTs are prepared by the instructors and teaching assistants. If a student team fails to outperform any VT, all team members will fail the project, resulting in an overall course failure. Potential deductions may include late project submissions (within three days) and inadequate documentation of key machine learning practice components.

Final course points are rounded to letter grades based on NTNU standard ranges. In the event that a student receives a final grade of 'F' or 'Fail,' they will be required to retake the entire course.

Course materials


  • Tom Mitchell: Machine learning, McGraw Hill, 1997.
  • Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006
  • Dipanjan Sarkar, Raghav Bali, Tushar Sharma: Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems, 2017

Selected papers and code examples.

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 2024

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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