Valentina Boeva: Catalogue data in Autumn Semester 2023 |
| 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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| 252-0945-17L | Doctoral Seminar Machine Learning (HS23)  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 credits | 1S | N. He, V. Boeva, J. M. Buhmann, R. Cotterell, T. Hofmann, A. Krause, G. Rätsch, M. Sachan, J. Vogt, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | An 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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 / Notice | This 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-4811-00L | 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
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