IBE400 Machine Learning (Autumn 2026)

This course provides an applied introduction to machine learning, offering students an overview of the fundamental concepts, techniques, and practical applications of machine learning. The course covers key topics such as supervised and unsupervised learning algorithms, including methods for classification, regression, and clustering.

The course places strong emphasis on exploratory data analysis to analyze datasets, identify patterns and relationships, and draw meaningful inferences from data. Students will also learn essential data preprocessing techniques, feature selection methods, and strategies for evaluating and validating machine learning models.

In addition, the course addresses important ethical considerations related to the development and use of machine learning systems.

Through a combination of lectures, practical exercises, and case studies, students will gain hands-on experience applying machine learning techniques to real-world problems.

Students are expected to have:

• Basic programming knowledge, preferably in Python or another high-level programming language, including familiarity with variables, control structures, and basic data structures.
• Introductory knowledge of statistics and probability, including concepts such as mean, variance, correlation, and basic statistical interpretation.
• Fundamental mathematical skills, particularly in algebra and basic linear algebra concepts (eg, vectors and matrices).
• Basic understanding of data handling, such as working with datasets, spreadsheets, or simple data analysis tools.
• General familiarity with computing environments, including installing software packages and using basic development tools (eg, Jupyter notebooks or similar platforms).

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 understanding of key machine learning techniques, including linear classification, logistic regression, decision trees, multi-class classification, and clustering methods.
• Apply machine learning algorithms to design and implement programs that address and solve real-world problems.

Competence

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

The course is primarily classroom-based, and students are encouraged to attend lectures and lab sessions in person in order to fully benefit from the learning activities. The course is organized with two hours of lectures and two hours of lab sessions per week. During the lab sessions, students will receive guidance and support from teaching assistants while working on practical exercises and applying machine learning techniques to datasets.

To support active learning, guided pre-class materials will be provided to help students prepare for upcoming topics. These materials are intended to introduce key concepts before the lectures, enabling students to engage more effectively in classroom discussions and practical activities. Students can expect to spend approximately one hour preparing for each class, as preparatory materials will be uploaded in advance.

In addition, guest lectures from industry experts are planned to provide students with exposure to how machine learning is applied in real-world industry settings. These sessions will offer insights into current practices, challenges, and emerging trends in the field.

For students who are unable to attend physically, lectures will be streamed online and recordings will be made available.

• Mandatory coursework: Assignments. Group of 1-2 students
• Coursework given: 2
• Coursework required: 2

Form of assessment: Digital School assessment

Proportion: 100%

Duration: 3 hours

Grouping: Individual

Grading scale: Letter (A - F)

Support material: None