Valentina Boeva: Katalogdaten im Herbstsemester 2023

NameFrau Prof. Dr. Valentina Boeva
LehrgebietBiomedizininformatik
Adresse
Professur für Biomedizininformatik
ETH Zürich, CAB G 32.2
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telefon+41 44 633 66 87
E-Mailvalentina.boeva@inf.ethz.ch
DepartementInformatik
BeziehungAssistenzprofessorin (Tenure Track)

NummerTitelECTSUmfangDozierende
252-0945-17LDoctoral Seminar Machine Learning (HS23) Belegung eingeschränkt - Details anzeigen
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 KP1SN. He, V. Boeva, J. M. Buhmann, R. Cotterell, T. Hofmann, A. Krause, G. Rätsch, M. Sachan, J. Vogt, F. Yang
KurzbeschreibungAn 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.
LernzielThe 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.
Voraussetzungen / BesonderesThis 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 Belegung eingeschränkt - Details anzeigen
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 KP2SV. Boeva, E. Krymova, L. Salamanca Miño
KurzbeschreibungSeminal and recent papers in machine learning are presented and discussed.
LernzielThe 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.
InhaltThe 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.
LiteraturThe papers will be presented and allocated in the first session of the seminar.
Voraussetzungen / BesonderesBasic 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 Belegung eingeschränkt - Details anzeigen
Only for Data Science MSc, Programme Regulations 2017.
14 KP9PA. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang
KurzbeschreibungIn 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.
LernzielThe 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.
Voraussetzungen / BesonderesPrerequisites: 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 Belegung eingeschränkt - Details anzeigen
Only for Data Science MSc, Programme Regulations 2023.
10 KPA. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang
KurzbeschreibungIn 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.
LernzielThe 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.
Voraussetzungen / BesonderesPrerequisites: 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 Belegung eingeschränkt - Details anzeigen 6 KP4GS. Sunagawa, P. Beltrao, V. Boeva, A. Kahles, C. von Mering, N. Zamboni
KurzbeschreibungStudents 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.
LernzielStudents 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.
Voraussetzungen / BesonderesCourse 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.
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Entscheidungsfindunggeprüft
Medien und digitale Technologiengeprüft
Problemlösunggeprüft
Projektmanagementgefördert
Soziale KompetenzenKommunikationgefördert
Kooperation und Teamarbeitgefördert
Persönliche KompetenzenAnpassung und Flexibilitätgefördert
Kreatives Denkengeprüft
Kritisches Denkengeprüft
Integrität und Arbeitsethikgefördert
Selbstbewusstsein und Selbstreflexion gefördert
Selbststeuerung und Selbstmanagement gefördert