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Applied Machine Learning

Master’s level
7,5 credits (ECTS)


The course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications.
During the course, a selection of topics will be covered in supervised learning, such as linear models for regression and classification, or nonlinear models such as neural networks, and in unsupervised learning such as clustering.
The use cases and limitations of these algorithms will be discussed, and their implementation will be investigated in programming assignments. Methodological questions pertaining to the evaluation of machine learning systems will also be discussed, as well as some of the ethical questions that can arise when applying machine learning technologies.
There will be a strong emphasis on the real-world context in which machine learning systems are used.
The use of machine learning components in practical applications will be exemplified, and realistic scenarios will be studied in application areas such as e-commerce, business intelligence, natural language processing, image processing, and bioinformatics. The importance of the design and selection of features, and their reliability, will be discussed.

Prerequisites and selection


To be eligible to the course, the student should have a Bachelor's degree in any subject, or have successfully completed 90 credits of studies in computer science, software engineering, or equivalent. Specifically, the course requires 7.5 credits programming, 7.5 credits introduction to data science or AI, such as DIT852 or DIT405, 7.5 credits calculus or mathematical modeling (such as DIT856), 7.5 credits probability theory, statistics, or mathematical statistics, such as DIT862. Alternatively have taken both of the following two courses: DIT847 and DIT278 (or equivalent) Applicants must prove knowledge of English: English 6/English B or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.