IBE400 Machine Learning (Autumn 2025)

ECTS Credits:
7.5
Responsible faculty:
Avdeling for logistikk
Campus:
Molde
Course Leader:
Swati Aggarwal
Teaching language:
English
Study level:
{code=LN, name=Foundation courses, level I}
Duration:
½ year
Schedule (TP) Reading list (Leganto)

Table of contents

Contents

This is an applied machine learning course that is designed to provide students with an overview of the fundamental concepts and techniques of machine learning. It covers a range of topics, including supervised and unsupervised learning, classification, regression, clustering, neural networks, and reinforcement learning. The course emphasizes the use of algorithms to analyze data sets, identify patterns and relationships, and make inferences based on that data. In addition to covering the basic principles of machine learning, the course also explores data preprocessing techniques, feature selection methods, and model evaluation strategies. Through a combination of lectures, practical exercises, and case studies, students will gain practical experience in applying machine learning techniques to real-world problems.

The course is connected to the following study programs

Recommended knowledge

You should have basic skills with programming languages such as Python, or R or Matlab, statistics and mathematics (including calculus, probability, and statistical inference).

It is also beneficial to have a background in data analysis and visualization.

The student's learning outcomes after passing the exam

Students will, after passing the examination, have

Knowledge

  • Solid foundation in the basic machine learning principles and practical skills.
  • Analyze data sets using a range of machine learning algorithms
  • Identify patterns and relationships in data
  • Understanding of the role of data preprocessing, feature selection, and model evaluation in machine learning

Skills

  • Demonstrate the ability to describe the machine learning techniques of Linear classification, Logistic regression, Decision Trees, Multi-class Classification, Naïve Bayes, Neural Networks, Clustering, Principal Component Analysis & Autoencoders, Support Vector Machines, Ensemble methods, Reinforcement Learning
  • Use machine learning algorithms to develop programs that effectively solve real-world problems.

Competence

  • Be able to apply ML techniques to real-world data sets.

Forms of teaching and learning

The course is entirely classroom-based, with all teaching and learning activities physically taking place. It is organized to have 2 hours of lecture and 2 hours of lab/group discussion/report writing exercise per week. During the lab exercise sessions, students will be supported by teaching assistants. The lectures or sessions will not be streamed or recorded.

Coursework requirements - conditions for taking the exam

  • Mandatory coursework: Assignments. Group of 1-3 students
  • Coursework given: 2
  • Coursework required: 2
  • Grading: Approved/Not approved

Exam

Form of assessment: Digital School assessment

Proportion: 100%

Duration: 3 hours

Grouping: Individual

Grading scale: Letter (A - F)

Support material: None

Course evaluation

Digital school exam at the end of the semester will be evaluated with a grade scale: Letter (A - F), and it will contribute 100% to the final grade for this course.

Pensum

The current reading list for 2025 Autumn can be found in Leganto
Last updated from FS (Felles studentsystem) Oct. 14, 2026 8:15:22 PM