Applied Machine Learning in Molecular Biotechnology

About this programme

Master's Programme, Molecular Biotechnology and Bioinformatics, year 1, Mandatory Please note: all information from the Course syllabus is available on this page in an accessible format. The course provides students with the skills to apply, improve, interpret, and evaluate machine learning methods in biotechnology. The course introduces practical applications of machine learning in genomics, transcriptomics, proteomics, and biomedical research. Students will learn the fundamentals of supervised and unsupervised learning as well as neural network architectures, with an emphasis on real-world applications. The lectures are combined with hands-on exercises, where theoretical foundations are integrated into biological and biomedical context. Theoretical and practical knowledge is deepened through a project in which students select relevant data and design, optimize, and apply a machine learning model. Completed degree project 15 credits, 20 credits in biotechnology, genomics, bio(medical) science, computer science or biostatistics, 6 credits in mathematics and English B/6. Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability. The examiner may apply another examination format when re-examining individual students. If the course is discontinued, students may request to be examined during the following two academic years.