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Machine Learning 7,5 Credits

Course Contents

Machine learning is a core subject within computer science and a branch of artificial intelligence. It focuses on how to design, implement, and evaluate algorithms that learn to improve their performance through experience. Research on machine learning is conducted in mathematics (computational learning theory), statistics (statistical learning), and computer science (empirical machine learning, data mining and knowledge discovery, pattern recognition, natural language processing, and computer vision). In addition, machine learning is an engineering discipline with practical applications to a multitude of challenges in the digital society.

This course introduces machine learning as a scientific research and engineering discipline. It provides students with the opportunity to gain a broad understanding of the discipline and its applications, as well as a basic understanding of its history, origins, and fundamental motivations.
More concretely, the course aims to teach students how to define tasks in a way which makes it possible to solve them wholly or partly with machine learning methodology. This entails the mapping of the task to a generic machine learning task and the identification of a suitable mechanism for learning from experience (data) to improve the performance at solving the task in question.

The course includes a series of lectures that highlight relevant topics from the course literature. Each lecture will focus on a specific learning paradigm or a general theme, such as: evaluation procedures and measures, experimentation, and software development and testing. Lab-based exercises will provide opportunities for students to solve basic machine learning problems under the supervision of a lab instructor. Students are then expected to complete individual assignments and participate in seminars to discuss their solutions. The last assignment is to conduct a project to be presented at the final seminar.


Passed courses at least 90 credits within the major subject Computer Engineering, Electrical Engineering (with relevant courses in Computer Engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering, and completed courses in Artificial Intelligence, 7,5 credits and Mathematics for Intelligent Systems, 7,5 credits or equivalent. Proof of English proficiency is required.

Level of Education: Master
Course code/Ladok code: TMLS22
The course is conducted at: School of Engineering

Previous and ongoing course occasions

Type of course
Study type
Spring 2023: Jan 16 - Mar 26
Rate of Study
Course coordinator
Florian Westphal
Florian Westphal
Tuition fees do NOT apply for EU/EEA citizens or exchange students
Application code
Last modified 2022-11-25 07:22:20