Julia Vogt: Catalogue data in Autumn Semester 2022

Name Prof. Dr. Julia Vogt
FieldMedical Data Science
Address
Professur für Medizin. Datenwiss.
ETH Zürich, CAB G 16.2
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telephone+41 44 633 87 14
E-mailjulia.vogt@inf.ethz.ch
URLhttps://mds.inf.ethz.ch
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
263-3300-00LData Science Lab Information Restricted registration - show details
Only for Data Science MSc.
14 credits9PC. Zhang, V. Boeva, R. Cotterell, A. Ilic, J. Vogt, F. Yang
AbstractIn this class, we bring together data science applications
provided by ETH researchers outside computer science and
teams of computer science master's students. Two to three
students will form a team working on data science/machine
learning-related research topics provided by scientists in
a diverse range of domains such as astronomy, biology,
social sciences etc.
ObjectiveThe goal of this class if for students to gain experience
of dealing with data science and machine learning applications
"in the wild". Students are expected to go through the full
process starting from data cleaning, modeling, execution,
debugging, error analysis, and quality/performance refinement.
Prerequisites / NoticePrerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing.
263-5100-00LTopics in Medical Machine Learning Restricted registration - show details
Number of participants limited to 18.

The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SG. Rätsch, J. Vogt
AbstractThis seminar discusses recent relevant contributions to the fields of medical machine learning and related areas. Each participant will hold a presentation and lead the subsequent discussion.
ObjectivePreparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to successfully present the key points of existing research work are the same as those needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class.
ContentThe topics covered in the seminar are related to recent computational challenges that arise in the medical field, including but not limited to clinical data analysis, interpretable machine learning, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are on the list of selected papers.
Prerequisites / NoticeKnowledge of machine learning and interest in applications in medicine. ML4H is beneficial as a prior course.