Valentina Boeva: Catalogue data in Autumn Semester 2024 |
Name | Prof. Dr. Valentina Boeva |
Field | Biomedical Informatics |
Address | Professur für Biomedizininformatik ETH Zürich, CAB G 32.2 Universitätstrasse 6 8092 Zürich SWITZERLAND |
valentina.boeva@inf.ethz.ch | |
Department | Computer Science |
Relationship | Assistant Professor (Tenure Track) |
Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||
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252-4811-00L | Machine Learning Seminar ![]() 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 credits | 2S | V. Boeva, E. Krymova, L. Salamanca Miño | |||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Seminal and recent papers in machine learning are presented and discussed. | ||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | The papers will be presented and allocated in the first session of the seminar. | ||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Basic knowledge of machine learning as taught in undergraduate courses such as "252-0220-00 Introduction to Machine Learning" are required. | ||||||||||||||||||||||||||||||||||||||||||||||||||
263-3300-00L | Data Science Lab ![]() ![]() Only for Data Science MSc, Programme Regulations 2017. | 14 credits | 9P | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||
Abstract | In 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 objective | The 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 / Notice | Prerequisites: 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
263-3300-10L | Data Science Lab ![]() ![]() Only for Data Science MSc, Programme Regulations 2023. | 10 credits | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang | ||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | In 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 objective | The 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 / Notice | Prerequisites: 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
263-5351-00L | Machine Learning for Genomics ![]() ![]() | 6 credits | 2V + 2U + 1A | V. Boeva | |||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course reviews solutions provided by machine learning to the most challenging questions in human genomics. | ||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Over 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 / Notice | Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line | ||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies![]() |
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551-1299-00L | Bioinformatics ![]() | 6 credits | 4G | S. Sunagawa, P. Beltrao, V. Boeva, A. Kahles, C. von Mering, N. Zamboni | |||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Students 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 objective | Students 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 / Notice | Course 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies![]() |
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