Valentina Boeva: Catalogue data in Autumn Semester 2024

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

NumberTitleECTSHoursLecturers
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.
Learning 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.
Learning 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.
Learning 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.
263-5351-00LMachine Learning for Genomics Information Restricted registration - show details 6 credits2V + 2U + 1AV. Boeva
AbstractThe course reviews solutions provided by machine learning to the most challenging questions in human genomics.
Learning objectiveOver the last few years, the parallel development of machine learning methods and molecular profiling technologies for human cells, such as sequencing, created an extremely powerful tool to get insights into the cellular mechanisms in healthy and diseased contexts. In this course, we will discuss the state-of-the-art machine learning methodology solving or attempting to solve common problems in human genomics. At the end of the course, you will be familiar with (1) classical and advanced machine learning architectures used in genomics, (2) bioinformatics analysis of human genomic and transcriptomic data, and (3) data types used in this field.
Content- Short introduction to major concepts of molecular biology: DNA, genes, genome, central dogma, transcription factors, epigenetic code, DNA methylation, signaling pathways
- Prediction of transcription factor binding sites, open chromatin, histone marks, promoters, nucleosome positioning (convolutional neural networks, position weight matrices)
- Prediction of variant effects and gene expression (hidden Markov models, topic models)
- Deconvolution of mixed signal (NMF, ICA)
- DNA, RNA and protein folding (RNN, LSTM, transformers)
- Data imputation for single-cell RNA-seq data, clustering and annotation (diffusion and methods on graphs)
- Batch correction (autoencoders, optimal transport)
- Survival analysis (Cox proportional hazard model, regularization penalties, multi-omics, multi-tasking)
Prerequisites / NoticeIntroduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Techniques and Technologiesfostered
Social CompetenciesCommunicationassessed
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.
Learning 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