CM2011 Applied Machine Learning and Artificial Intelligence 7.5 credits

In this course, the students will learn about the relationship between data, models and algorithms, to understand how to process and draw conclusions of data through data mining and machine learning. The course introduces some theory on machine learning, but focuses mainly on current applied methods. Successful machine learning applications need to be designed through a critical engagement and understanding of data, the algorithms that can be applied based on the kind of features the data exhibits and choosing the right paradigm of machine learning. This course provides a fundamental basis for using machine learning in an ethical and responsible manner. What are the predominant paradigms in machine learning and in what situations are they best used? What perspectives should we consider when we design machine learning applications? Why is a critical perspective important for developing machine learning?

AK2036 Theory and Methodology of Science with Applications (Natural and Technological Science)7.5 credits

The aim of the course is to provide a deeper understanding of the methodological and underlying philosophical issues that arise in science, and inspire to reflection on such issues within the student’s own area of study. After having taken the course the student should have acquired basic knowledge of the foundational issues in the methodology and philosophy of science, specially as regards the natural and technological sciences.

CM2013 Signal Processing and Data Analytics in Biomedical Engineering 7.5 credits

In this course, the students will learn about methods and techniques for data acquisition, preprocessing and noise reduction, pattern recognition and feature extraction and basic machine learning and classification methods to specific biomedical applications based on required specifications and constraints. The course is divided into theory lectures, computer exercises and lab and project work.