One Year Master Programme in Computer Science, specialisation in Machine Learning

Education facts

Code: TDGH6

The programme focuses on techniques and applications in machine learning and relevant research methods in the field. The programme consists of four in-depth courses, one application course, one method course and a degree project in computer science with a focus on machine learning comprising 15 credits. Semester 1 Artificial Intelligence 7.5 credits Fundamentals of Machine Learning 7.5 credits Deep Learning 7.5 credits Edge Machine Learning and the Internet of Things 7.5 credits. Semester 2 Applied machine learning 7.5 credits Research methods for machine learning 7.5 credits Master‘s thesis in computer science with focus on machine learning 15 credits The order of the courses may change and some course names may be updated. The first course in the programme, Fundamentals of Machine Learning, provides an introduction to various basic machine learning techniques and different data processing methods used in the field. The Artificial Intelligence course puts machine learning in a broader perspective. It covers the history of artificial intelligence and the relationship between human intelligence, cognition and neurocomputing. The course also provides an overview of areas such as natural language processing (NLP), computer vision and robotics. In addition, the course highlights various ethical issues in artificial intelligence. Edge Machine Learning and the Internet of Things is another important area covered in the programme. The course highlights how machine learning algorithms can run on computing devices at the periphery of a computer network to make decisions and predictions as close to the original data source as possible. This technology places new demands on developers, including managing resource constraints that can affect computational power and security. The course Deep Learning introduces students to an area of machine learning that involves training artificial neural networks to learn from data. The neural networks used in deep learning are inspired by the structure and function of the human brain. The course also covers current research in this area. Applied Machine Learning is a course that highlights how machine learning is used in different application areas and in research. The course also covers data preparation, model selection and evaluation. The course examines various case studies where machine learning has been applied and the challenges that arose during the process. The course focuses on different machine learning models, evaluation of these models and their suitability for intended research contexts. The course Research Methods for Machine Learning deals with methods that are an important part of research in machine learning. Examples of important aspects of this kind of research include collecting and preparing data in a correct way. The course covers data collection methods, cleaning and handling of lost data. The course also examines ethical issues related to machine learning research are considered. In the Master’s Thesis in Computer Science with a Focus on Machine Learning, students formulate and carry out an independent project in the computer science research area of machine learning. In this course, students use both methodological and subject competence to develop and answer their own questions through empirical work.

Entry requirements

Bachelor‘s Degree in Computer Science or other equivalent field alternatively Bachelor‘s Degree in Computer Science and Engineering at least 22.5 credits in programming and 22,5 credits in mathematics of which at least 3.5 credits in statistics and verified knowledge of English corresponding to the course English 6 in the Swedish upper secondary school or the equivalent

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• One Year Master Programme in Computer Science, specialisation in Machine Learning

  Kristianstad University

  Start date: 1 September 2025

  End date: 7 June 2026

  Pace of study: 100 %