Credits: 5

Schedule: 18.04.2019 - 23.05.2019

Contact information for the course (applies in this implementation): Talayeh Aledavood

Teaching Period (valid 01.08.2018-31.07.2020): 

Announced later

Learning Outcomes (valid 01.08.2018-31.07.2020): 

You are familiar with some scientifically or technically demanding topic.

Content (valid 01.08.2018-31.07.2020): 

This course has a varying topic. The content of the course is a selected current topic areas in complex systems. When arranged, the course may be given in English. Information about the arrangement and the beginning of the course will be published in the web pages.

Details on the course content (applies in this implementation): 

Digital  health is a new and fast growing field where technology meets health.  In addition to use technology 
to enhance human health, this field tries to make medicine more 
personalized. In this course, we will review methods and recent advances
in the field of digital health and personalized medicine with a focus 
on the quantification of human behavior (with the tools of data science
and machine learning). This course will give an  overview of devices and
sensors, methods and computational tools, and  different areas where
digital devices are used to enhance people’s  health and well-being. 
After the initial lectures by the instructor, students will pick the
topic that they are mostly interested in. Each topic comes with a list
of articles which can help students to understand their topics better
and dig into it deeper. Each student will give an oral presentation
about  their chosen topic and write a final report in form of a review
article at the end of the course. As part of the the assignments of the
course there will be exercises to improve scientific writing skills and
learning how to  write a review paper. The lectures will be held during
period V at 14:15 - 16:00, in R030/T6 A136.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Announced later.

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 


In this version of the course there is no PASS/FAIL option! A grade between 0-5 is given to the students based on the returned
assignments, oral presentation(s),
final report, and participation in peer-evaluation. The breakdown of
grades: weekly assignments 20% (graded by the instructor), Oral
presentation 20% (graded by peers and instructor), Evaluating peers
-both oral presentations and final reports (2-3 reports
per student) 15% (graded by the instructor), final report 45% (graded
by peers and the instructor).
Please note that in addition to lecture attendance,  doing the oral presentation and returning the final report
are mandatory for passing the course. 

Details on calculating the workload (applies in this implementation): 

the lectures (6 total) is compulsory (one lecture can be missed if the
is previously notified and if it is not the week of your own
presentation). During the first session each student will pick a topic
to work on in depth based on recent literature. Each week there will be
some general assignments (to be returned in the form
of small reports). Each student picks one week to give a detailed
presentation about their topic. As the final project of the course, each
student will work on a report about their assigned topic in the format
of a review paper. Students will also evaluate other students’
presentations and reports.

Details on the course materials (applies in this implementation): There will be scientific articles provided for each of the topics.

Grading Scale (valid 01.08.2018-31.07.2020): 

0-5, may also be graded with pass/fail.

Further Information (valid 01.08.2018-31.07.2020): 

The content of the course varies.

Additional information for the course (applies in this implementation): 


course does not have any formal requirements, however it is mostly
targeted towards MSc students
who have at least completed one year of their studies and PhD students. For this course some familiarity with statistical learning methods, data science, or machine learning is needed. The course will
require reading, understanding and summarizing research papers for the
purpose of giving presentations about them and writing
a final review paper. So it is not recommended for students who do not have any previous experience with writing longer reports or summary of research outcomes. There will however be assignments in the course which will help the students to improve their scientific writing skills. If you are unsure whether you have the necessary background/skills to take this course, please contact the instructor: talayeh,

Details on the schedule (applies in this implementation): 

Thu 18.04.2019 at 14:15 - 16:00, R030/T6 A136
Thu 25.04.2019 at 14:15 - 16:00, R030/T6 A136
Thu 02.05.2019 at 14:15 - 16:00, R030/T6 A136
Thu 09.05.2019 at 12:30 - 14:15, A211
Thu 16.05.2019 at 14:15 - 16:00, R030/T6 A136
Thu 23.05.2019 at 14:15 - 16:00, R030/T6 A136


Registration and further information