Search result: Catalogue data in Spring Semester 2023
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| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 261-5113-00L | Computational Challenges in Medical Genomics  | W | 2 credits | 2S | A. Kahles | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to succesfully present the key points of existing research work are the same as needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The topics covered in the seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are amongst the list of selected papers. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 263-5002-00L | Generative Visual Models 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. | W | 4 credits | 2S + 2A | T. Hofmann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar investigates generative models for image synthesis, which can be controlled via language prompts and visual seeding. The relevant methods will be explained in a few initial classes. Participants will study the research literature and develop project ideas in small groups, which will then be implemented. Presentation of research papers, project ideas, and results is a key component. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this class is for participants to find, read, understand and critically assess research literature in order to reach the current state of knowledge in the field. Moreover, the project work aims to enrich these readings by hands-on experience and allows for student to develop creative ideas of their own. This is meant to provide a wholistic research experience in small teams. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Phase 1: Introduction & Background During the first weeks of the semester lectures will provide the technical background to understand visual generative models. This includes a historic overview as well as technical deep dives into specialized topics such as stable diffusion and contrastive learning. There will also be a tutorial on suitable software framework to explore and fine-tune such models. Each participant will do a graded pen & paper exercise in order to check on progress. 20% of the grade, correctness of questions. Phase 2: Reading & Planning In the second phase, participants will split up in teams (ideal size 3) and will perform independent reading and planning towards a project idea. Paper suggestions and project sketches will be distributed to provide guidance and inspiration. During this time, participants are also expected to familiarize themselves with the experimental setup (we will locally host models on our GPU servers) and perform some simple warm-up or proof-of-concept experiments to inform the project definition. Each group will give a 15+5 min project pitch and will give/receive feedback from other teams. 30% of the grade, creativity of the idea, clarity of project articulation, recognition of existing work. Phase 3: Project Execution & Presentation In the third phase, teams will implement their project and run the designed experiments to answer the articulated research questions or goals. Participants will have (limited) access to local GPU servers. Each group will produce a written project report and will deliver a presentation. 50% of the grade, success of the project, quality of the experiments, quality of the slides/presentation. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This hybrid course unit is open to master students enrolled in the Computer Science or Data Science Master program. Enrollement is limited to 20 students. A sufficient background in machine learning (e.g. 252-0220-00L Intro ML, 252-0535-00L Advanced ML) is assumed. The work load during Phase 1-2 will be moderate, but during Phase 3, we expect more intense team work. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 263-5225-00L | Advanced Topics in Machine Learning and Data Science The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | F. Perez Cruz | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this seminar, recent papers of the machine learning and data science literature are presented and discussed. Possible topics cover statistical models, machine learning algorithms and its applications. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The seminar “Advanced Topics in Machine Learning and Data Science” familiarizes students with recent developments in machine learning and data science. Recently published articles, as well as influential papers, have to be presented and critically reviewed. The students will learn how to structure a scientific presentation, which covers the motivation, key ideas and main results of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth for the audience to be able to follow its main conclusion, especially why the article is (or is not) worth attention. The presentation style will play an important role and should reach the level of professional scientific presentations. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar will cover a number of recent papers which have emerged as important contributions to the machine learning and data science literatures. The topics will vary from year to year but they are centered on methodological issues in machine learning and its application, not only to text or images, but other scientific domains like medicine, climate or physics. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The papers will be presented in the first session of the seminar. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 401-3620-22L | Student Seminar in Statistics: Causality Number of participants limited to 76. Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. Also offered in the Master Programmes Statistics resp. Data Science. | W | 4 credits | 2S | P. L. Bühlmann, N. Meinshausen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Causality is dealing with fundamental questions about cause and effect. The student seminar covers statistical and mathematical aspects of causality ranging from fundamental formalization of concepts to practical algorithms and methods. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The participants of the seminar acquire knowledge about: concepts and formalization of statistical causality; methods, algorithms and corresponding assumptions for inferring causal relations from data; causal analysis in practice based on real data. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic course in probability and statistics. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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