Valentina Boeva: Catalogue data in Autumn Semester 2020

Name	Prof. Dr. Valentina Boeva
Field	Biomedical Informatics
Address	Professur für Biomedizininformatik ETH Zürich, CAB G 32.2 Universitätstrasse 6 8092 Zürich SWITZERLAND
Telephone	+41 44 633 66 87
E-mail	valentina.boeva@inf.ethz.ch
Department	Computer Science
Relationship	Assistant Professor (Tenure Track)

Number	Title	ECTS	Hours	Lecturers
252-4811-00L	Machine Learning Seminar Number of participants limited to 24. 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, T. Hofmann
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.
261-5100-00L	Computational Biomedicine Number of participants limited to 60.	5 credits	2V + 1U + 1A	G. Rätsch, V. Boeva, N. Davidson
Abstract	The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
Learning objective	Over the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.
Content	The course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine: 1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches. 2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes. 3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.
Prerequisites / Notice	Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
263-3300-00L	Data Science Lab Only for Data Science MSc.	14 credits	9P	C. Zhang, 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.