PHVIT9520 Intervensjoner i helsevitenskap Emneplan

The course consists of lectures, assignments, group consultations, presentation seminars, and project work. In the seminars, students will read papers, present, and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.

The following required coursework must be approved before the student can take the exam:

• Two oral presentations (one on a given topic, one on the topic of own choice)
• Participate as a prepared opponent/discussant in two presentations from other students

There is a minimum 80% mandatory attendance in this course. Students who do not meet this requirement will not be allowed to sit the exam.

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate

• is at the forefront of knowledge of conceptual understanding and of theories related to research into different types of health interventions
• can evaluate the usefulness and application of different designs, methods, and processes in research related to health interventions
• can contribute towards developing new knowledge, theories, methods, and interpretations of positive and negative results of health interventions
• can evaluate the usefulness and application of relevant guidelines, best practice reports, and different types of systematic review

Skills

The PhD candidate can

• address complex issues related to developing, planning, and evaluating interventions
• address issues related to the connection between the theoretical basis of interventions, measures, and evaluation
• address complex issues related to analytical and ethical considerations in health intervention research and is able to challenge established knowledge and practice in the field

General competence

The PhD candidate can

• present and discuss knowledge about intervention in the health sciences
• can assess the need for, and initiate, innovations in intervention

All aids are permitted for the project report, provided the rules for plagiarism and source referencing are complied with.

No aids are permitted for the oral exam.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Associate Professor Raju Shrestha

• Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
• Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.

This course covers principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in various areas such as computer vision, surveillance, assistive technology, medical imaging, etc. Therefore, the course intends to provide case studies and examples of ML and DL in solving various problems. Students can explore the tremendous potential of modern AI, ML, and DL methods and techniques in solving problems in different application domains through project work.