course-details-portlet

TDT4172

Introduction to Machine Learning

Credits 7.5
Level Third-year courses, level III
Course start Autumn 2026
Duration 1 semester
Language of instruction Norwegian
Location Trondheim
Examination arrangement School exam - multiple choice

About

About the course

Course content

The course gives a basic introduction to data analysis and machine learning. It covers the learning regimes of supervised and unsupervised learning thoroughly, and a light introduction to reinforcement learning and explanation methods for machine learning models. The course work is project driven with focus on applications, using Python and commonly used machine learning libraries.

Learning outcome

Knowledge: Fundamentals of machine learning with commonly used learning algorithms.

Skills: Ability to analyse data sets, and train and evaluate machine learning models on data. Evaluate adequateness of learning regimes based on the data.

General competencies: Understand the basic principles of data analysis and machine learning. Knowledge about the applicability and limitations of different contemporary learning algorithms.

Learning methods and activities

Lectures, self-study. Compulsory activity in the form of assignments, will be published during the semester. These must be passed to gain admittance to the final exam.

Compulsory assignments

  • Mandatory assignments

Further on evaluation

The re-sit examination is held in August.

If there is a re-sit examination, the examination form may be changed from written (multiple choice) to oral.

Course materials

Hands-on Machine Learning with Scikit Learn, Keras and Tensorflow, 2022, Aurelien Geron

Credit reductions

Course code Reduction From
BBAN3001 3.5 sp Autumn 2025
IDATG2208 7.5 sp Autumn 2025
TTT4185 7.5 sp Autumn 2026
This course has academic overlap with the courses in the table above. If you take overlapping courses, you will receive a credit reduction in the course where you have the lowest grade. If the grades are the same, the reduction will be applied to the course completed most recently.

Subject areas

  • Computer and Information Science

Contact information

Course coordinator

Department with academic responsibility

Department of Computer Science

Timetable

Timetable

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Show detailed timetable in TP Download .ics file iCal

Examination

Examination

Examination arrangement: School exam - multiple choice
Grade: Letter grades

Ordinary examination - Autumn 2026

School exam - multiple choice
Weighting 100/100 Examination aids Code D Duration 4 hours Exam system Inspera Assessment Place and room Not specified yet.

Re-sit examination - Summer 2027

School exam - multiple choice
Weighting 100/100 Examination aids Code D Duration 4 hours Exam system Inspera Assessment Place and room Not specified yet.

All about examinations at NTNU