ACIT4730 Biomedical Engineering Projects Course description

This course covers topics selected to be of particular relevance for students with their intended Master thesis. Material for the course is put together in cooperation with the thesis main-supervisor, and the course proceeds on a self-study basis under the supervision. The course has also a practical project that lasts for a short period where the students are assigned at any of laboratories at OsloMet, medical/health care companies, or a department at local hospitals in the Oslo and Akershus region.

No formal requirements over and above the admission requirements.

A student who has completed this course should have the following learning outcomes:

Knowledge:

Upon successful completion of the course:

• the student will have in-depth knowledge of the selected topic in the curriculum and its relation to the practical project work.

Skills:

Upon successful completion of the course, the student:

• can apply theoretical knowledge into practical settings.
• can analyze a practical challenge in the view of the theoretical and research-based methodologies.

General competence:

Upon successful completion of the course, the student:

• is enabled to analyze, present and debate specific research subjects in light of the theoretical and practical approaches.
• can discuss the subject both at expert and non-expert levels.

The course applies research-based learning supervised by one or more supervisors (internal/external), and a practical assignment with individual report work. The student will make a written report based on research problems from the practical assignment and the selected theoretical material. The course ends by the student/students in group (2-5 students) giving a presentation on the chosen topic and the practical assignments.

None

An individual/group (2-5 students) project report (at least 15 pages and max 30 pages) on the chosen topic and practical assignment, and an oral examination with two examiners.

50% of the grade is based on the individual/groups (2-5 students) project report and presentation (1). 50% of the grade is based on oral examination (2).

Both exams must be passed in order to pass the course.

The oral exam cannot be appealed.

The class covers foundations and recent advances of Machine Learning from the point of view of Statistical Learning Theory. The goal of this class is to provide students with the practical skillset to support the theoretical knowledge acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Access to good statistical/data analysis software is paramount. Therefore, we will illustrate the use of the models throughout the course with real implementation.

No formal requirements over and above the admission requirements.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• will have a good understanding the different concepts and methods of supervised and unsupervised statistical learning and how to apply them on large data.
• has advanced knowledge of probabilistic formulation of the various learning problems

Skills

Upon successful completion of the course, the student:

• can apply different high-dimensional regression techniques on data
• can apply different classification techniques on data
• can apply clustering techniques on data
• can derive learning algorithms for new models and analyze new data with them.
• can apply dimensionality reduction techniques on data

General competence

Upon successful completion of the course, the student:

• can apply different predictive models on data and assess their performance
• can use supervised and unsupervised learning in different real life problem