Valentina Boeva: Catalogue data in Autumn Semester 2023

Name Prof. Dr. Valentina Boeva
FieldBiomedical Informatics
Address
Professur für Biomedizininformatik
ETH Zürich, CAB G 32.2
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telephone+41 44 633 66 87
E-mailvalentina.boeva@inf.ethz.ch
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
252-0945-17LDoctoral Seminar Machine Learning (HS23) Restricted registration - show details
Only for Computer Science Ph.D. students.

This doctoral seminar is intended for PhD students affiliated with the Institute for Machine Learning. Other PhD students who work on machine learning projects or related topics need approval by at least one of the organizers to register for the seminar.
2 credits1SN. He, V. Boeva, J. M. Buhmann, R. Cotterell, T. Hofmann, A. Krause, G. Rätsch, M. Sachan, J. Vogt, F. Yang
AbstractAn essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
ObjectiveThe seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills.
Prerequisites / NoticeThis doctoral seminar of the Machine Learning Laboratory of ETH is intended for PhD students who work on a machine learning project, i.e., for the PhD students of the ML lab.
252-4811-00LMachine Learning Seminar Restricted registration - show details
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SV. Boeva, E. Krymova, L. Salamanca Miño
AbstractSeminal and recent papers in machine learning are presented and discussed.
ObjectiveThe seminar familiarizes students with advanced and recent ideas in machine learning. Original articles have to be presented, contexctualized, and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper.
ContentThe seminar will cover a number of recent papers which have emerged as important contributions in the machine learning research community. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications.
LiteratureThe papers will be presented and allocated in the first session of the seminar.
Prerequisites / NoticeBasic knowledge of machine learning as taught in undergraduate courses such as "252-0220-00 Introduction to Machine Learning" are required.
263-3300-00LData Science Lab Information Restricted registration - show details
Only for Data Science MSc, Programme Regulations 2017.
14 credits9PA. Ilic, V. Boeva, R. Cotterell, 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.
263-3300-10LData Science Lab Information Restricted registration - show details
Only for Data Science MSc, Programme Regulations 2023.
10 creditsA. Ilic, V. Boeva, R. Cotterell, 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.
551-1299-00LBioinformatics Restricted registration - show details 6 credits4GS. Sunagawa, P. Beltrao, V. Boeva, A. Kahles, C. von Mering, N. Zamboni
AbstractStudents will study bioinformatic concepts in the areas of metagenomics, genomics, transcriptomics, proteomics, biological networks and biostatistics. Through integrated lectures, practical hands-on exercises and project work, students will also be trained in analytical and programming skills to meet the emerging increase in data-driven knowledge generation in biology in the 21st century.
ObjectiveStudents will have an advanced understanding of the underlying concepts behind modern bioinformatic analyses at genome, metagenome and proteome-wide scales. They will be familiar with the most common data types, where to access them, and how to analytically work with them to address contemporary questions in the field of biology.
Prerequisites / NoticeCourse participants have already acquired basic programming skills in UNIX, Python and R.

Students bring their own computer with keyboard, internet access (browser) and software to connect to the ETH network via VPN.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered