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Data Science

Data Science

0590b_MA120

  • Mobile Communications

    0089cA3.3
    • 19303901 Lecture
      Mobile Communications (Jochen Schiller)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      The module mobile communication presents major topics from mobile and wireless communications - the key drivers behind today's communication industry that influence everybody's daily life. 

      The whole lecture focuses on a system perspective giving many pointers to real systems, standardization and current research.

      The format of the lecture is the flipped classroom, i.e., you should watch the videos of a lecture BEFORE participating in the Q&A session. We will then discuss all open issues, answer questions etc. during the Q&A session.

      Main topics of the lecture are:

      • Basics of wireless transmission: frequencies, signals, antennas, multiplexing, modulation, spread spectrum
      • Medium access: SDMA, FDMA, TDMA, CDMA;
      • Wireless telecommunication systems: GSM, TETRA, IMT-2000, LTE, 5G
      • Wireless local area networks: infrastructure/ad-hoc, IEEE 802.11/15, Bluetooth, ZigBee
      • Mobile networking: Mobile IP, ad-hoc networks
      • Mobile transport layer: traditional TCP, additional mechanisms
      • Outlook: 5 to 6G, low power wireless networks

      Suggested reading

      Jochen Schiller, Mobilkommunikation, Addison-Wesley, 2.Auflage 2003

      Alle Unterlagen verfügbar unter http://www.mi.fu-berlin.de/inf/groups/ag-tech/teaching/resources/Mobile_Communications/course_Material/index.html

  • Applied Machine Learning in Bioinformatics

    0262cD1.12
    • 19403613 Lab Seminar
      Machine Learning and AI in the Life Sciences: Methods and Applications (Tim Conrad, Christoph Tycowicz)
      Schedule: Fr 08:00-12:00 (Class starts on: 2025-04-25)
      Location: T9/049 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Prerequisites:

      Attended the Statistics course from the Master in Bioinformatics FU (or equivalent)

      Comments

      This course introduces key machine learning and AI techniques with a strong focus on their applications in the life sciences. You will learn how to analyze complex biomedical data, from pre-processing to model selection and interpretation, using techniques such as dimensionality reduction, feature selection, supervised and unsupervised learning, visual analysis, and deep learning.

      Through hands-on projects based on real-world datasets, you will gain practical experience in selecting and applying the right methods to tackle key challenges in the life sciences and bioinformatics, such as disease prediction, biomarker discovery, and patient (status) classification.  These projects will reinforce your learning while also helping you develop both technical expertise and scientific communication skills through presentations.

      By the end of the course, you will be able to process and model life science data, evaluate AI-driven insights, and effectively communicate your findings. Prior experience in programming (e.g., in R, Python, Java, or C/C++) is highly recommended.
    • 60103513 Lab Seminar
      Computer vision for biomedical images (Sören Lukassen)
      Schedule: Do 14:00-18:00 (Class starts on: 2025-04-17)
      Location: A6/017 Frontalunterrichtsraum (Bioinf) (Arnimallee 6)

      Comments

      Imaging techniques have become an integral component of both biomedical research and clinical practice. At the same time, automated image analysis has experienced significant progress, driving technologies ranging from image search to autonomous vehicles. This automation is increasingly applied to biomedical images. However, adapting computer vision algorithms for biomedical images introduces unique challenges due to their distinct properties uncommon in other imaging datasets.

      In this course, you will explore the prevalent imaging modalities within biomedicine, including CT scans, MRI, ultrasound, and whole-slide microscopy images. We will investigate the common data formats for these images, understanding what distinguishes them from typical jpeg or png files, and how these distinctions can facilitate their efficient analysis. After an introduction to basic computer vision algorithms, we will trace the evolution of classification and segmentation models in this domain over the last decade, starting with convolutional neural networks and culminating with cutting-edge architectures such as vision transformers. Through practical exercises, you will apply your knowledge to a dataset of histology slide images from cancer patients, aiming to predict the tumor's stage and location. Additionally, we will investigate how your models arrive at their predictions, identifying the data patterns they consider informative and connecting these insights to the pathophysiological alterations within tumor tissues.

      Our models will be built using the pytorch package in Python. While familiarity with coding neural networks is not a prerequisite, prior experience in general Python programming is expected.

      By the conclusion of this course, you will be equipped to develop segmentation and classification models for biomedical images, recognize common pitfalls and artifacts, explain how your models arrive at their predictions, and effectively communicate your findings.

  • Machine Learning in Bioinformatics

    0262cD1.7
    • 19405701 Lecture
      Machine Learning in Bioinformatics (Philipp Florian Benner, Hugues Richard)
      Schedule: Mo 08:00-10:00 (Class starts on: 2025-04-14)
      Location: A6/SR 025/026 Seminarraum (Arnimallee 6)

      Comments

      This course introduces key machine learning concepts and is accompanied by tutorials and exercises where machine learning methods are applied to actual bioinformatics problems. After a short recap of probability theory, we introduce probabilistic methods for classification and sequence analysis (Naive Bayes, Mixture Models, Hidden Markov Models). We discuss Expectation Maximization (EM) from a probabilistic perspective and use it for sequence analysis. Linear and logistic regression serve as an entry point to more complex machine learning methods, including kernel methods and neural networks. The lecture covers multiple neural network architectures (CNNs, GNN, Transformers) that are currently used in the bioinformatics community and other research domains. During the tutorials and as part of homework assignments, selected machine learning models are implemented in Python using scikit-learn and pytorch. The course should enable students to understand all common machine learning techniques and devise state of the art classification strategies that can then be applied to problems in bioinformatics and related fields.
      Contents:
      - Naive Bayes
      - Clustering and Mixture Models
      - Hidden Markov Models
      - Regression and Partial Least Squares
      - Kernel Methods
      - Neural Networks and Architectures
      - Regularization and Model Selection   Requirements:
      - Linear algebra (basic vector and matrix algebra)
      - Analysis (mathematical optimization, Lagrange)
      - Programming in Python -- including object oriented programming
      - A basic understanding or keen interest in molecular biology and bioinformatics applications

    • 19405702 Practice seminar
      Practice Seminar for Machine Learning in Bioinformatics (Philipp Florian Benner, Hugues Richard)
      Schedule: Mi 08:00-10:00 (Class starts on: 2025-04-16)
      Location: A7/SR 031 (Arnimallee 7)

  • Data Science in the Life Sciences

    0590bB1.1
    • 19405606 Seminar-style instruction
      Data Science in the Life Sciences (Katharina Jahn)
      Schedule: Mo 10:00-14:00 (Class starts on: 2025-04-14)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

      Comments

      This course offers an introduction to various types of data and analysis techniques which are typically used in the life sciences (e.g. omics technologies). The goal is to get a deeper understanding of advanced concepts and data analytical methods in the area of life sciences.

      The focus will be on the following topics:

      * acquisition and pre-processing of data from the area of life sciences,
      * explorative analysis techniques,
      * concepts and tools for reproducible research,
      * theory and practice of methods and models for the analysis of data from the life sciences (statistical inference, regression models, methods of machine learning),
      * introduction to methods of big data analysis.

      After successful completion of this course, participants are able to evaluate, plan and conduct investigations in the life sciences using common methods.

       
    • 19405612 Project Seminar
      Projectseminar for Data Science in the Life Sciences (Katharina Jahn)
      Schedule: Mi 10:00-14:00 (Class starts on: 2025-04-16)
      Location: A6/SR 031 Seminarraum (Arnimallee 6)

  • Interdisciplinary Apporaches (Data Science) A

    0590bB1.27
    • 24206501 Lecture
      V - Geographische Informationssysteme I: Grundlagen (Fabian Faßnacht)
      Schedule: Do 14:00-16:00 (Class starts on: 2025-04-17)
      Location: G 202 Hörsaal Geographie (Malteserstr. 74-100 G)

      Comments

      Die Studierenden verfügen über kartographisches Basiswissen insbesondere im Hinblick auf verschiedene Karten-
      typen, die Projektionen, Koordinatensysteme, den Kartenaufbau sowie Kartenwerke und sind in der Lage, Karten
      zu interpretieren. Sie besitzen die Fähigkeit, geowissenschaftliche Fragestellungen eigenständig mittels Geogra-
      phischen Informationssystemen computergestützt zu bearbeiten und Ergebnisse zu präsentieren.
      Inhalte:
      Die Studierenden erhalten eine Einführung in die Kartographie mit den Themenbereichen allgemeine Grundlagen der
      Kartographie, thematische/topographische Karten, Kartennetzentwürfe und Koordinatensysteme, Partizipative Geogra-
      phische Informationssystems (PGIS), Generalisierung sowie Karteninterpretation. Anhand ausgewählter Anwendungs-
      beispiele werden grundlegende Konzepte von Geographischen Informationssystemen und der Geoinformationsverar-
      beitung computergestützt vermittelt: Struktur und Anwendungen von Geoinformationssystemen, grundlegende Konzepte
      wie Datenmodelle, Erfassung von Geodaten, Methoden und Probleme der Abbildung des Georaumes, Georeferenzie-
      rung, Extraktion und Verarbeitung räumlicher Daten, Methoden der räumlichen und geostatistischen Analyse und Inter-
      polationsverfahren, Erstellung und Analyse digitaler Geländemodelle, Visualisierung von Geodaten einschließlich Karten.

  • Ethical Foundations of Data Science

    0590bB1.3

  • Special Aspects of Data Science in Life Sciences

    0590bB1.4
    • 19304701 Lecture
      Robotics (Daniel Göhring)
      Schedule: Mi 12:00-14:00 (Class starts on: 2025-04-16)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Additional information / Pre-requisites

      Students interested in robotics with application to autonomous vehicles. Voraussetzungen: As a prerequisite, student should have basic knowledge of maths, in particular linear algebra and a bit of optimization. Students will work with a real model car in the robotics lab.

      Comments

      Content

      This class will give an introduction to robotics. It will be structured into the following parts:

      • Generating motion and and dynamic control: This chapter will cover coordinate frames, non-holonomic constraints, Ackermann-drive (in analogy to street cars), PID.
      • Planning: Planning around obstacles, path finding, Dijkstra, A*, configuration space obstacles, RRTs, lattice planners, gradient methods, potential fields, splines.
      • Localization and mapping: state estimation problem, Bayesian filter, Odometry, Particle & Kalman filter, Extended and Unscented Kalman-Filter, simultaneous localization and mapping (SLAM).
      • Vision and perception: SIFT, HOG-features, Deformable parts models, hough transform, lane detection, 3d-point clouds, RANSAC .

      After these lectures, students will be able to design basic algorithms for motion, control and state estimation for robotics.

      The lecture will be in German, accompanying materials in English.

      Suggested reading

      Literatur:


      John J Craig: Introduction to Robotics: Mechanics and Control; Steven LaValle: Planning Algorithms; Sebastian Thrun, Wolfram Burgard, Dieter Fox: Probabilistic Robotics

       
    • 19321101 Lecture
      Advanced Data Structures (László Kozma)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A3/ 024 Seminarraum (Arnimallee 3-5)

      Comments

      Efficient data structures are important components of all nontrivial algorithms, and are basic building blocks of the modern computing infrastructure. Besides their practical importance, the design and analysis of data structures has revealed a rich mathematical theory. The ultimate theoretical limits of data structures are the subject of deep open questions.

      The topic of this course is the design and analysis of advanced data structures (including both classical and recent results).
      An earlier course with a similar selection of topics can be seen here:
      https://page.mi.fu-berlin.de/lkozma/ds2020

      Familiarity with algorithmic and relevant mathematical concepts is assumed (e.g., the course "Advanced algorithms" or similar as a prerequisite).

       

      Suggested reading

      D. E. Knuth, The Art of Computer Programming, Volume 4A: Combinatorial Algorithms, Part 1. (Addison-Wesley, 2011), xv+883pp. ISBN 0-201-03804-8
    • 19333101 Lecture
      Cybersecurity and AI II: Explainability (Gerhard Wunder)
      Schedule: Mo 12:00-14:00, Di 10:00-12:00, Fr 12:00-14:00 (Class starts on: 2025-04-15)
      Location: 1.3.21 Seminarraum T1 (Arnimallee 14)
    • 19336901 Lecture
      Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)

      Comments

      The lecture on Advanced Data Visualization for Artificial Intelligence is a comprehensive exploration of state-of-the-art techniques and tools to create and validate complex visualizations for communicating data insights and stories, with a specific focus on applications in Natural Language Processing (NLP) and Explainable AI. The lecture will introduce participants to the nested model of visualization, which encompasses four layers: characterizing the task and data, abstracting into operations and data types, designing visual encoding and interaction techniques, and creating algorithms to execute techniques efficiently. This model will serve as a framework for designing and validating data visualizations.

      Furthermore, the lecture will delve into the application of data visualization in NLP, emphasizing the visualization of word embeddings and language models to aid in the exploration of semantic relationships between words and the interpretation of language model behavior. In the context of Explainable AI, the focus will be on using visualizations to explain model predictions and feature importance, thereby enhancing the interpretability of AI models. By leveraging the nested model of visualization and focusing on NLP and Explainable AI, the lecture aims to empower participants with the essential skills to design and validate advanced data visualizations tailored to these specific applications, ultimately enabling them to effectively communicate complex data patterns and gain deeper insights from their data.
    • 60102501 Lecture
      Resampling techniques and their application (Frank Konietschke)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

      Comments

      In this course, we introduce resampling techniques for analyzing trials with small sample sizes. Special attention will be given to both estimation methods as well as inference procedures. We hereby will find answers to the questions (1) "How does resampling work?" and "When does resampling work"? Throughout the class we will study one sample, two samples and even factorial designs with independent and dependent observations. All algorithms will be presented and illustrated using R statistical software. Knowledge of fundamentals in statistical testing as well as basic skills in R are recommended and prerequisite. 
    • 60102701 Lecture
      Complex Data Analysis in Physiology (Dorothee Günzel)
      Schedule: Mo 14:30-18:30 (Class starts on: 2025-04-14)
      Location: keine Angabe

      Comments

      Joint class taught by the Institute of Clinical Physiology and the Institute of Physiology at the Charité.

      Theoretical and practical aspects of data acquisition, real-time data processing and automated pattern recognition in biomedicine. Topics from the following areas are covered in depth:

      • Data acquisition and processing of image files in research and clinical settings (e.g. live cell imaging, super-resolution microscopy, medical imaging techniques).
      • Electrophysiological methods (e.g. impedance spectroscopy, microarrays, EEG, ECG)
      • Methods and application of automated pattern recognition (e.g. automated tumour detection, real-time analysis of biological signals in the brain-computer interface or in retina implants, prediction of individual arrhythmia risks)

      The course will be split into two segments: the first seven appointments in the semester will take place at the Institute of Physiology, while the second seven appointments will take place at the Institute of Clinical Physiology.

      For further information: http://klinphys.charite.de/bioinfo/ or mail to Dorothee Günzel
    • 19304702 Practice seminar
      Practice seminar for Robotics (Daniel Göhring)
      Schedule: Do 12:00-14:00 (Class starts on: 2025-04-17)
      Location: T9/049 Seminarraum (Takustr. 9)
    • 19321102 Practice seminar
      Practice seminar for Advanced Data Structures (N.N.)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: A3/019 Seminarraum (Arnimallee 3-5)

      Comments

      Übungen
    • 19333102 Practice seminar
      Practice seminar for Cybersecurity and AI II (Gerhard Wunder)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-04-28)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)
    • 19336902 Practice seminar
      Ü: Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    • 60102502 Practice seminar
      Practice Seminar for Resampling techniques and their application (Frank Konietschke)
      Schedule: Mi 16:00-18:00 (Class starts on: 2025-04-16)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)
    • 60102702 Practice seminar
      Practice seminar for Complex Data Analysis in Physiology (Dorothee Günzel)
      Schedule: s. Vorlesung
      Location: keine Angabe

  • Current Research Topics: Data Science in the Life Sciences

    0590bB1.5
    • 19321101 Lecture
      Advanced Data Structures (László Kozma)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A3/ 024 Seminarraum (Arnimallee 3-5)

      Comments

      Efficient data structures are important components of all nontrivial algorithms, and are basic building blocks of the modern computing infrastructure. Besides their practical importance, the design and analysis of data structures has revealed a rich mathematical theory. The ultimate theoretical limits of data structures are the subject of deep open questions.

      The topic of this course is the design and analysis of advanced data structures (including both classical and recent results).
      An earlier course with a similar selection of topics can be seen here:
      https://page.mi.fu-berlin.de/lkozma/ds2020

      Familiarity with algorithmic and relevant mathematical concepts is assumed (e.g., the course "Advanced algorithms" or similar as a prerequisite).

       

      Suggested reading

      D. E. Knuth, The Art of Computer Programming, Volume 4A: Combinatorial Algorithms, Part 1. (Addison-Wesley, 2011), xv+883pp. ISBN 0-201-03804-8
    • 19325301 Lecture
      Cluster Computing (Barry Linnert)
      Schedule: Di 10:00-12:00 (Class starts on: 2025-04-15)
      Location: T9/055 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Target group

      • Computer Science Master students

      Requirements

      • Experience with computers and software as well as programing skills.

      Language

      • The course language is German (or English if requested).
      • The exam will be formulated in German, but answers may be given in English, too.

      Credits & Exams

      The criteria for gaining credits are

      • active participation in the tutorials: regular preparation of assignements & presentation of results in the tutorials
      • passing of the exam

      Website

      https://www.mi.fu-berlin.de/w/SE/VorlesungClusterComputing

       

      Comments

      Cluster computer are the prevailing type of high performance computers. They are built of custom off-the-shelf processor boards that are connected by a high speed interconnection network. Although usually locally integrated, they are conceptually distributed systems with local operating system images. Their enormous potential, however, can only be exploited, if program code and data are optimally distributed across the nodes. Cluster management mechanisms also need to be scalable to be employed in systems with thousands of nodes. The lecture course gives an overview of the architecture of cluster computers and the related management problems for which algorithmic solutions are presented.

      Suggested reading

      • Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
      • Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
      • Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
      • Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
      • Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999
    • 19333101 Lecture
      Cybersecurity and AI II: Explainability (Gerhard Wunder)
      Schedule: Mo 12:00-14:00, Di 10:00-12:00, Fr 12:00-14:00 (Class starts on: 2025-04-15)
      Location: 1.3.21 Seminarraum T1 (Arnimallee 14)
    • 60102701 Lecture
      Complex Data Analysis in Physiology (Dorothee Günzel)
      Schedule: Mo 14:30-18:30 (Class starts on: 2025-04-14)
      Location: keine Angabe

      Comments

      Joint class taught by the Institute of Clinical Physiology and the Institute of Physiology at the Charité.

      Theoretical and practical aspects of data acquisition, real-time data processing and automated pattern recognition in biomedicine. Topics from the following areas are covered in depth:

      • Data acquisition and processing of image files in research and clinical settings (e.g. live cell imaging, super-resolution microscopy, medical imaging techniques).
      • Electrophysiological methods (e.g. impedance spectroscopy, microarrays, EEG, ECG)
      • Methods and application of automated pattern recognition (e.g. automated tumour detection, real-time analysis of biological signals in the brain-computer interface or in retina implants, prediction of individual arrhythmia risks)

      The course will be split into two segments: the first seven appointments in the semester will take place at the Institute of Physiology, while the second seven appointments will take place at the Institute of Clinical Physiology.

      For further information: http://klinphys.charite.de/bioinfo/ or mail to Dorothee Günzel
    • 19321102 Practice seminar
      Practice seminar for Advanced Data Structures (N.N.)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: A3/019 Seminarraum (Arnimallee 3-5)

      Comments

      Übungen
    • 19325302 Practice seminar
      Practice seminar for Cluster Computing (Barry Linnert)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: T9/K44 Rechnerpoolraum (Takustr. 9)
    • 19333102 Practice seminar
      Practice seminar for Cybersecurity and AI II (Gerhard Wunder)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-04-28)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)
    • 60102702 Practice seminar
      Practice seminar for Complex Data Analysis in Physiology (Dorothee Günzel)
      Schedule: s. Vorlesung
      Location: keine Angabe

  • Seminar: Data Science in the Life Sciences (for Master's Students)

    0590bB1.6
    • 19306711 Seminar
      Seminar on Algorithms (László Kozma)
      Schedule: Do 14:00-16:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-17)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      Contents

      Advanced topics in algorithm design with a changing focus. The topic is determined in each semester.

      This semester we plan a reading-group-style seminar on recent breakthrough results (2020-2025) in shortest paths algorithms.

      Target audience

      Masters students in computer science and mathematics.

      Recommended prerequisites

      "Advanced algorithms" or a similar class.

      Suggested reading

      Spezialliteratur aus Zeitschriften
    • 19333611 Seminar
      Seminar: Deep Learning for biomedical applications (Vitaly Belik)
      Schedule: Mo 16:00-18:00 (Class starts on: 2025-04-14)
      Location: T9/053 Seminarraum (Takustr. 9)

      Comments

      Recent developments in the area of machine learning due to availability of data and computational power promise to revolutionize almost every area of science. The driving technology behind this advancement is deep learning – a machine learning technology based on artificial neural networks consisting of many layers. Deep learning is capable of processing huge amount of data of different nature and already outperforming humans in many decision-making tasks. Biomedical research became now a source of large heterogeneous data, i.e. images, video, activity sensors, omics and text data. Leveraging the opportunities of this deep learning technology in the biomedical field requires particular set of skills combining thorough knowledge of necessary algorithms, specifics of biomedical data and designated programming tools. In this course we aim to offer students with background in computer science an opportunity to acquire the above skills to be able to deploy deep learning technology with a focus on biomedical applications. The course is structured as a seminar, where students under extensive guidance of instructors read fundamental books and recent research articles on deep learning, learn necessary programming tools, and produce their own implementations of computational pipelines in case studies using already published or original data. Starting from fundamental aspects of deep learning we aim to cover its applications to e.g. image data, time series data, text data, complex networks.

      Suggested reading

      [1] Andresen N, Wöllhaf M, Hohlbaum K, Lewejohann L, Hellwich O, Thöne- Reineke C, Belik V, Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expres- sion analysis. Plos One, 15(4):e0228059, (2020) https://doi.org/10.1371/ journal.pone.0228059

      [2] Jarynowski A, Semenov A, Kamiński M, Belik V. Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning. J Med Internet Res 2021;23(11):e30529 https://doi.org//10.2196/30529
    • 19334617 Seminar / Undergraduate Course
      Seminar/Proseminar: How to Startup (Tim Landgraf)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: T9/046 Seminarraum (Takustr. 9)

      Comments

      This seminar explores the multifaceted world of startups, providing students with a comprehensive understanding of what it takes to succeed in a dynamic and competitive environment. Topics covered include team composition, market analysis, investment logic, emerging trends (such as AI), and common pitfalls faced by startups.
      Unlike traditional seminars, this course emphasizes practical engagement. Students will work on preparing concise "Impulsvorträge" (short, 15-minute talks) on specific startup-related topics. These presentations will draw from a variety of sources, including:

      * Web Research: Gathering insights from industry reports, blogs, and articles.
      * Interviews: Engaging with actual startups to gain firsthand knowledge and perspectives.
      * Trend Analysis: Examining current innovations and disruptions in the startup ecosystem.


      Each talk will serve as the starting point for an interactive discussion, stimulating deeper understanding and diverse viewpoints among participants.
      This seminar is ideal for students who are curious about entrepreneurship and eager to explore how startups operate, grow, and navigate challenges in today's fast-paced world.

       
    • 19335011 Seminar
      Seminar: Networks, dynamic models and ML for data integration in the life sciences (Katharina Baum)
      Schedule: Di 12:00-14:00 (Class starts on: 2025-04-15)
      Location: T9/137 Konferenzraum (Takustr. 9)

      Comments

      Research seminar of the group Data Integration in the Life Sciences (DILiS). Also open for seminar participation in the Master's program, online participation possible. Please refer to the current schedule on the whiteboard!

      The seminar offers space for the discussion of advanced and integrative data analysis techniques, in particular presentations and discussion of ongoing or planned research projects, news from conferences, review and discussion of current literature and discussion of possible future teaching formats and content, and presentations, as well as final presentations on theses or project seminars. The seminar language is mostly English. Interested students are welcome to attend and drop in without obligation or present a topic of their own choice of interest to the working group as in a usual seminar. Please note: Individual dates may be canceled or postponed. Please contact me in case of questions (katharina.baum@fu-berlin.de)!
    • 19336717 Seminar / Undergraduate Course
      Active learning, uncertainty and XAI with applications in biomedicine (Katharina Baum)
      Schedule: Di 14:00-16:00 (Class starts on: 2025-04-15)
      Location: T9/051 Seminarraum (Takustr. 9)

      Comments

      In this advanced seminar, we will discuss a variety of methods for machine learning. The focus will be on approaches to active learning, uncertainty estimation and its utilization, as well as methods for explaining models. The application and development of these methods for biomedical research questions will be explored using current research papers.

      Examples of approaches covered include:

      • selective sampling
      • SHAP values
      • Gaussian ensemble models
      • Bayesian neural networks

      The seminar will primarily be conducted in English, but of course, you are welcome to ask questions in German.
    • 19337517 Seminar / Undergraduate Course
      Seminar/Proseminar: Time Series Learning (Manuel Heurich)
      Schedule: Mo 10:00-12:00 (Class starts on: 2025-04-14)
      Location: A7/SR 140 Seminarraum (Hinterhaus) (Arnimallee 7)

      Comments

      This seminar focuses on Machine Learning approaches that specialize in sequential data. Most real-world data is acquired over time. Moreover, most of the available data is not image data. We will discuss works before the Transformer era (e.g., RNNs, LSTMs) and highlight their strengths and weaknesses outside the Computer Vision domain. More recently, transformer-based approaches have outperformed earlier methods. We selectively pick works that highlight their strength in knowledge discovery on sequential data. With the strong trend towards powerful multi-modal models, the seminar aims to introduce state-of-the-art methods to produce robust embeddings based on Time Series data.
    • 19405211 Seminar
      Seminar for Complex Systems in Bioinformatics (Martin Vingron, Max von Kleist, Jana Wolf)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: A3/SR 119 (Arnimallee 3-5)
    • 19406411 Seminar
      Journal Club: Public Health Data Science (Max von Kleist)
      Schedule: Time: Wedneysday 10:00am First occasion: 23.April Last occasion: 23.July
      Location: Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77

      Comments

      In this seminar, current research in the field of data-driven public health science, as well as the progress reports of PhD students and post-docs, will be presented. Master's students will present either an assigned journal article or their master's thesis, or they will report on their research internship. Credits will be awarded for article presentations only.

      Schedule: online

      Meeting link: https://rki.webex.com/rki/j.php?MTID=mab87f7d0fb44ca448f53d4a69c179d77 Time: Wedneysday 10:00am   First occasion: 23.April Last occasion: 23.July

    • 60103311 Seminar
      Spatial Sequencing Analysis (Journal Club) (Christian Conrad)
      Schedule: Fr 10:00-12:00 (Class starts on: 2025-04-25)
      Location: A6/SR 009 Seminarraum (Arnimallee 6)

      Comments

      Abstrakt:  In principle, NGS sequencing is also an imaging technology. Latest methods allow to sequence DNA, RNA and proteins in tissues/cells directly and in combination. Together we will identify recent publications, especially how to process and analyze these spatial sequencing data sets with machine learning. All participants have to give 1-2 talks themselves. Please find more details: https://iimaging.org/openings/ For question or more information please contact: christian.conrad@bih-charite.de Dates by arrangement.

  • Selected Topics in Data Science in Life Sciences

    0590bB1.7
    • 19405201 Lecture
      Complex Systems in Bioinformatics (Martin Vingron, Max von Kleist, Jana Wolf)
      Schedule: Di 12:00-14:00 (Class starts on: 2025-04-15)
      Location: A3/SR 120 (Arnimallee 3-5)

      Comments

      Students have acquired a deeper understanding of fundamental mathematical and algorithmic concepts in the field of modeling, simulation and analysis of complex biological systems against the background of current research trends in system biology and biotechnology. They are capable of analyzing a given biological or medical problem, selecting a suitable modeling approach, independently developing a solution and assessing and communicating the results.

      Content:

      Topics from the following areas are considered in depth:

      - Network structure analysis

      - Graphical modeling

      - Modeling of biochemical networks using standard differential equations

      - Discrete modeling of regulatory networks

      - Constraint-based modeling

      - Stochastic and hybrid modeling

      Suggested reading

      wird in der Veranstaltung bekanntgegeben.
    • 19405202 Practice seminar
      Practice seminar for Complex Systems in Bioinformatics (Martin Vingron, Max von Kleist, Jana Wolf)
      Schedule: Di 14:00-16:00 (Class starts on: 2025-04-15)
      Location: A3/SR 120 (Arnimallee 3-5)

  • Special Aspects of Data Science Technologies

    0590bB2.3
    • 19304701 Lecture
      Robotics (Daniel Göhring)
      Schedule: Mi 12:00-14:00 (Class starts on: 2025-04-16)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Additional information / Pre-requisites

      Students interested in robotics with application to autonomous vehicles. Voraussetzungen: As a prerequisite, student should have basic knowledge of maths, in particular linear algebra and a bit of optimization. Students will work with a real model car in the robotics lab.

      Comments

      Content

      This class will give an introduction to robotics. It will be structured into the following parts:

      • Generating motion and and dynamic control: This chapter will cover coordinate frames, non-holonomic constraints, Ackermann-drive (in analogy to street cars), PID.
      • Planning: Planning around obstacles, path finding, Dijkstra, A*, configuration space obstacles, RRTs, lattice planners, gradient methods, potential fields, splines.
      • Localization and mapping: state estimation problem, Bayesian filter, Odometry, Particle & Kalman filter, Extended and Unscented Kalman-Filter, simultaneous localization and mapping (SLAM).
      • Vision and perception: SIFT, HOG-features, Deformable parts models, hough transform, lane detection, 3d-point clouds, RANSAC .

      After these lectures, students will be able to design basic algorithms for motion, control and state estimation for robotics.

      The lecture will be in German, accompanying materials in English.

      Suggested reading

      Literatur:


      John J Craig: Introduction to Robotics: Mechanics and Control; Steven LaValle: Planning Algorithms; Sebastian Thrun, Wolfram Burgard, Dieter Fox: Probabilistic Robotics

       
    • 19321101 Lecture
      Advanced Data Structures (László Kozma)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A3/ 024 Seminarraum (Arnimallee 3-5)

      Comments

      Efficient data structures are important components of all nontrivial algorithms, and are basic building blocks of the modern computing infrastructure. Besides their practical importance, the design and analysis of data structures has revealed a rich mathematical theory. The ultimate theoretical limits of data structures are the subject of deep open questions.

      The topic of this course is the design and analysis of advanced data structures (including both classical and recent results).
      An earlier course with a similar selection of topics can be seen here:
      https://page.mi.fu-berlin.de/lkozma/ds2020

      Familiarity with algorithmic and relevant mathematical concepts is assumed (e.g., the course "Advanced algorithms" or similar as a prerequisite).

       

      Suggested reading

      D. E. Knuth, The Art of Computer Programming, Volume 4A: Combinatorial Algorithms, Part 1. (Addison-Wesley, 2011), xv+883pp. ISBN 0-201-03804-8
    • 19327401 Lecture
      Image- and video coding (Heiko Schwarz)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-04-14)
      Location: T9/053 Seminarraum (Takustr. 9)

      Comments

      This course introduces the most important concepts and algorithms that are used in modern image and video coding approaches. We will particularly focus on techniques that are found in current international video coding standards.

      In a short first part, we introduce the so-called raw data formats, which are used as input and output formats of image and video codecs. This part covers the following topics:

      • Colour spaces and their relation to human visual perception
      • Transfer functions (gamma encoding)
      • Why do we use the YCbCr format?

      The second part of the course deals with still image coding and includes the following topics:

      • The start: How does JPEG work?
      • Why do we use the Discrete Cosine Transform?
      • Efficient coding of transform coefficients
      • Prediction of image blocks
      • Adaptive block partitioning
      • How do we take decisions in an encoder?
      • Optimized quantization

      In the third part, we discuss approaches that make video coding much more efficient than coding all pictures using still image coding techniques:

      • Motion-compensated prediction
      • Coding of motion vectors
      • Algorithms for motion estimation
      • Sub-sample accurate motion vectors and interpolation filters
      • Usage of multiple reference pictures
      • What are B pictures and why do we use them?
      • Deblocking and deringing filters
      • Efficient temporal coding structures

      In the exercises, we will implement our own image codec (in a gradual manner). We may extend it to a simple video codec.

       

      Suggested reading

      • Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
      • Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
      • Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
      • Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
      • Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
      • Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.
    • 19333101 Lecture
      Cybersecurity and AI II: Explainability (Gerhard Wunder)
      Schedule: Mo 12:00-14:00, Di 10:00-12:00, Fr 12:00-14:00 (Class starts on: 2025-04-15)
      Location: 1.3.21 Seminarraum T1 (Arnimallee 14)
    • 19336901 Lecture
      Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)

      Comments

      The lecture on Advanced Data Visualization for Artificial Intelligence is a comprehensive exploration of state-of-the-art techniques and tools to create and validate complex visualizations for communicating data insights and stories, with a specific focus on applications in Natural Language Processing (NLP) and Explainable AI. The lecture will introduce participants to the nested model of visualization, which encompasses four layers: characterizing the task and data, abstracting into operations and data types, designing visual encoding and interaction techniques, and creating algorithms to execute techniques efficiently. This model will serve as a framework for designing and validating data visualizations.

      Furthermore, the lecture will delve into the application of data visualization in NLP, emphasizing the visualization of word embeddings and language models to aid in the exploration of semantic relationships between words and the interpretation of language model behavior. In the context of Explainable AI, the focus will be on using visualizations to explain model predictions and feature importance, thereby enhancing the interpretability of AI models. By leveraging the nested model of visualization and focusing on NLP and Explainable AI, the lecture aims to empower participants with the essential skills to design and validate advanced data visualizations tailored to these specific applications, ultimately enabling them to effectively communicate complex data patterns and gain deeper insights from their data.
    • 19337401 Lecture
      Elliptic Curve Cryptography (Marian Margraf)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: T9/SR 005 Übungsraum (Takustr. 9)
    • 19304702 Practice seminar
      Practice seminar for Robotics (Daniel Göhring)
      Schedule: Do 12:00-14:00 (Class starts on: 2025-04-17)
      Location: T9/049 Seminarraum (Takustr. 9)
    • 19321102 Practice seminar
      Practice seminar for Advanced Data Structures (N.N.)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: A3/019 Seminarraum (Arnimallee 3-5)

      Comments

      Übungen
    • 19327402 Practice seminar
      Practice seminar for image- und video coding (Heiko Schwarz)
      Schedule: Mo 12:00-14:00 (Class starts on: 2025-04-14)
      Location: T9/053 Seminarraum (Takustr. 9)
    • 19333102 Practice seminar
      Practice seminar for Cybersecurity and AI II (Gerhard Wunder)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-04-28)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)
    • 19336902 Practice seminar
      Ü: Advanced Data Visualization for Artificial Intelligence (Georges Hattab)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)
    • 19337402 Practice seminar
      Tutorials for Elliptic Curve Cryptography (Marian Margraf)
      Schedule: Do 14:00-16:00 (Class starts on: 2025-04-17)
      Location: T9/K40 Multimediaraum (Takustr. 9)

  • Current Research Topics in Data Science Technologies

    0590bB2.4
    • 19321101 Lecture
      Advanced Data Structures (László Kozma)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A3/ 024 Seminarraum (Arnimallee 3-5)

      Comments

      Efficient data structures are important components of all nontrivial algorithms, and are basic building blocks of the modern computing infrastructure. Besides their practical importance, the design and analysis of data structures has revealed a rich mathematical theory. The ultimate theoretical limits of data structures are the subject of deep open questions.

      The topic of this course is the design and analysis of advanced data structures (including both classical and recent results).
      An earlier course with a similar selection of topics can be seen here:
      https://page.mi.fu-berlin.de/lkozma/ds2020

      Familiarity with algorithmic and relevant mathematical concepts is assumed (e.g., the course "Advanced algorithms" or similar as a prerequisite).

       

      Suggested reading

      D. E. Knuth, The Art of Computer Programming, Volume 4A: Combinatorial Algorithms, Part 1. (Addison-Wesley, 2011), xv+883pp. ISBN 0-201-03804-8
    • 19325301 Lecture
      Cluster Computing (Barry Linnert)
      Schedule: Di 10:00-12:00 (Class starts on: 2025-04-15)
      Location: T9/055 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Target group

      • Computer Science Master students

      Requirements

      • Experience with computers and software as well as programing skills.

      Language

      • The course language is German (or English if requested).
      • The exam will be formulated in German, but answers may be given in English, too.

      Credits & Exams

      The criteria for gaining credits are

      • active participation in the tutorials: regular preparation of assignements & presentation of results in the tutorials
      • passing of the exam

      Website

      https://www.mi.fu-berlin.de/w/SE/VorlesungClusterComputing

       

      Comments

      Cluster computer are the prevailing type of high performance computers. They are built of custom off-the-shelf processor boards that are connected by a high speed interconnection network. Although usually locally integrated, they are conceptually distributed systems with local operating system images. Their enormous potential, however, can only be exploited, if program code and data are optimally distributed across the nodes. Cluster management mechanisms also need to be scalable to be employed in systems with thousands of nodes. The lecture course gives an overview of the architecture of cluster computers and the related management problems for which algorithmic solutions are presented.

      Suggested reading

      • Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
      • Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
      • Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
      • Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
      • Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999
    • 19327401 Lecture
      Image- and video coding (Heiko Schwarz)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-04-14)
      Location: T9/053 Seminarraum (Takustr. 9)

      Comments

      This course introduces the most important concepts and algorithms that are used in modern image and video coding approaches. We will particularly focus on techniques that are found in current international video coding standards.

      In a short first part, we introduce the so-called raw data formats, which are used as input and output formats of image and video codecs. This part covers the following topics:

      • Colour spaces and their relation to human visual perception
      • Transfer functions (gamma encoding)
      • Why do we use the YCbCr format?

      The second part of the course deals with still image coding and includes the following topics:

      • The start: How does JPEG work?
      • Why do we use the Discrete Cosine Transform?
      • Efficient coding of transform coefficients
      • Prediction of image blocks
      • Adaptive block partitioning
      • How do we take decisions in an encoder?
      • Optimized quantization

      In the third part, we discuss approaches that make video coding much more efficient than coding all pictures using still image coding techniques:

      • Motion-compensated prediction
      • Coding of motion vectors
      • Algorithms for motion estimation
      • Sub-sample accurate motion vectors and interpolation filters
      • Usage of multiple reference pictures
      • What are B pictures and why do we use them?
      • Deblocking and deringing filters
      • Efficient temporal coding structures

      In the exercises, we will implement our own image codec (in a gradual manner). We may extend it to a simple video codec.

       

      Suggested reading

      • Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
      • Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
      • Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
      • Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
      • Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
      • Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.
    • 19333101 Lecture
      Cybersecurity and AI II: Explainability (Gerhard Wunder)
      Schedule: Mo 12:00-14:00, Di 10:00-12:00, Fr 12:00-14:00 (Class starts on: 2025-04-15)
      Location: 1.3.21 Seminarraum T1 (Arnimallee 14)
    • 19337401 Lecture
      Elliptic Curve Cryptography (Marian Margraf)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: T9/SR 005 Übungsraum (Takustr. 9)
    • 19321102 Practice seminar
      Practice seminar for Advanced Data Structures (N.N.)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: A3/019 Seminarraum (Arnimallee 3-5)

      Comments

      Übungen
    • 19325302 Practice seminar
      Practice seminar for Cluster Computing (Barry Linnert)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: T9/K44 Rechnerpoolraum (Takustr. 9)
    • 19327402 Practice seminar
      Practice seminar for image- und video coding (Heiko Schwarz)
      Schedule: Mo 12:00-14:00 (Class starts on: 2025-04-14)
      Location: T9/053 Seminarraum (Takustr. 9)
    • 19333102 Practice seminar
      Practice seminar for Cybersecurity and AI II (Gerhard Wunder)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-04-28)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)
    • 19337402 Practice seminar
      Tutorials for Elliptic Curve Cryptography (Marian Margraf)
      Schedule: Do 14:00-16:00 (Class starts on: 2025-04-17)
      Location: T9/K40 Multimediaraum (Takustr. 9)

  • Selected Topics in Data Science Technologies (A)

    0590bB2.5
    • 19315401 Lecture
      Multiplicative Weights - A Popular Algorithmic Technique with Countless Applications (Wolfgang Mulzer)
      Schedule: Di 14:00-16:00, Fr 10:00-12:00 (Class starts on: 2025-04-15)
      Location: T9/055 Seminarraum (Takustr. 9)

      Comments

      Just like greedy algorithms, dynamic programming, or divide-and-conquer, the multiplicative weights method is a fundamental algorithmic technique with countless applications across disciplines. However, it is taught only rarely in basic classes.



      In this class, we will study the multiplicative weights method in detail. We will learn about the basic technique and its variations, explore connections to other fields such as online convex optimization and machine learning, and see the beautiful mathematics that lies behind it.



      We will also see many applications of the technique, with examples from combinatorial optimization, machine learning, algorithmic game theory, computational geometry, information theory, online algorithms, and many more. For some of the applications, we will have invited speakers who have applied the technique in their respective fields.



      The class is jointly attended by students at Sorbonne Paris Nord in Paris and will be given in a hybrid format.



      The course website can be found here: https://www.inf.fu-berlin.de/lehre/SS25/mwu/


      Suggested reading

      Wird noch bekannt gegeben.
    • 19326601 Lecture
      Markov Chains (Katinka Wolter)
      Schedule: Di 12:00-14:00, Do 10:00-12:00 (Class starts on: 2025-04-15)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Comments

      In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

      Suggested reading

      William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.
    • 19315402 Practice seminar
      Practice seminar for Multiplicative Weights (Michaela Krüger)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: A7/SR 140 Seminarraum (Hinterhaus) (Arnimallee 7)
    • 19326602 Practice seminar
      Practice seminar for Markov Chains (Justus Purat)
      Schedule: Di 14:00-16:00 (Class starts on: 2025-04-15)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)

  • Selected Topics in Data Science Technologies (B)

    0590bB2.6
    • 19315401 Lecture
      Multiplicative Weights - A Popular Algorithmic Technique with Countless Applications (Wolfgang Mulzer)
      Schedule: Di 14:00-16:00, Fr 10:00-12:00 (Class starts on: 2025-04-15)
      Location: T9/055 Seminarraum (Takustr. 9)

      Comments

      Just like greedy algorithms, dynamic programming, or divide-and-conquer, the multiplicative weights method is a fundamental algorithmic technique with countless applications across disciplines. However, it is taught only rarely in basic classes.



      In this class, we will study the multiplicative weights method in detail. We will learn about the basic technique and its variations, explore connections to other fields such as online convex optimization and machine learning, and see the beautiful mathematics that lies behind it.



      We will also see many applications of the technique, with examples from combinatorial optimization, machine learning, algorithmic game theory, computational geometry, information theory, online algorithms, and many more. For some of the applications, we will have invited speakers who have applied the technique in their respective fields.



      The class is jointly attended by students at Sorbonne Paris Nord in Paris and will be given in a hybrid format.



      The course website can be found here: https://www.inf.fu-berlin.de/lehre/SS25/mwu/


      Suggested reading

      Wird noch bekannt gegeben.
    • 19326601 Lecture
      Markov Chains (Katinka Wolter)
      Schedule: Di 12:00-14:00, Do 10:00-12:00 (Class starts on: 2025-04-15)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Comments

      In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

      Suggested reading

      William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.
    • 19315402 Practice seminar
      Practice seminar for Multiplicative Weights (Michaela Krüger)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: A7/SR 140 Seminarraum (Hinterhaus) (Arnimallee 7)
    • 19326602 Practice seminar
      Practice seminar for Markov Chains (Justus Purat)
      Schedule: Di 14:00-16:00 (Class starts on: 2025-04-15)
      Location: A6/SR 007/008 Seminarraum (Arnimallee 6)

  • Seminar: Data Science Technology

    0590bB2.7
    • 19303811 Seminar
      Project Seminar: Data Management (Muhammed-Ugur Karagülle)
      Schedule: Do 12:00-14:00 (Class starts on: 2025-04-17)
      Location: T9/137 Konferenzraum (Takustr. 9)

      Additional information / Pre-requisites

      Requirement

      ALP I-III, Foundations of Datenbase Systems, good programming knowledge.

      Comments

      Content

      A project seminar serves as preparation of a thesis (bachelor or master) in the AGDB. The focus of this project seminar lies on the analysis and visualization of medical data. Additionally, we will realize a small software project.

      Suggested reading

      Wird bekannt gegeben.
    • 19306711 Seminar
      Seminar on Algorithms (László Kozma)
      Schedule: Do 14:00-16:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-17)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      Contents

      Advanced topics in algorithm design with a changing focus. The topic is determined in each semester.

      This semester we plan a reading-group-style seminar on recent breakthrough results (2020-2025) in shortest paths algorithms.

      Target audience

      Masters students in computer science and mathematics.

      Recommended prerequisites

      "Advanced algorithms" or a similar class.

      Suggested reading

      Spezialliteratur aus Zeitschriften
    • 19310817 Seminar / Undergraduate Course
      Seminar/Proseminar: High Performance and Cloud Computing (Barry Linnert)
      Schedule: Di 12:00-14:00 (Class starts on: 2025-04-22)
      Location: T9/K40 Multimediaraum (Takustr. 9)

      Comments

      When it comes to processing complex applications or large amounts of data within a reasonable time frame, the use of parallel programs is unavoidable. However, these can be very different due to the specific application framework or the technical environments. For example, high-performance computing (HPC) uses supercomputers that support applications with a high degree of interaction, while cloud computing focuses on the provision of data and computing capacity on demand.
      Both application areas have challenges both at the programming level and in the administration of the corresponding systems.
      In the seminar, we will focus on one aspect of this spectrum and summarize and evaluate current research in this area.

      Further information on the procedure will be provided at the first meeting on 22.04.2025.
    • 19328217 Seminar / Undergraduate Course
      Seminar/Proseminar: New Trends in Information Systems (Agnès Voisard)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: A3/SR 119 (Arnimallee 3-5)

      Comments

      This seminar aims at studying recent trends in data management. Among others, we will look at two emerging topics, namely Location-Based Services (LBS) and Event-Based Services (EBS).

      Event-based Systems (EBS) are part of many current applications such as business activity monitoring, stock tickers, facility management, data streaming, or security. In the past years, the topic has gained increasing attention from both the industrial and the academic community. Current research concentrates of diverse aspects that range from event capture (incoming data) to response triggering. This seminar aims at studying some of the current trends in Event-based Systems with a strong focus on models and design. Location-based services are now often part of every day's life through applications such as navigation assistants in the public or private transportation domain. The underlying technology deals with many different aspects, such as location detection, information retrieval, or privacy. More recently, aspects such as user context and preferences were considered in order to send users more personalized information.

      A solid background in databases is required, typically a database course at a bachelor level.

      Suggested reading

      Wird bekannt gegeben.
    • 19333311 Seminar
      Seminar: Continual Learning (Manuel Heurich)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: A7/SR 140 Seminarraum (Hinterhaus) (Arnimallee 7)

      Comments

      This seminar focuses on recent advances in ‘Continual Learning’, an increasingly important field within machine learning. Continual Learning tackles the problem of drifting data in input space and changes between input and target distribution. Static models drop significantly in performance when data distributions are subject to change over time. We will cover recent approaches that tackle this problem from different angles. This seminar explores the training of adaptive models that can perform strongly in highly volatile domains.
    • 19334617 Seminar / Undergraduate Course
      Seminar/Proseminar: How to Startup (Tim Landgraf)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-04-16)
      Location: T9/046 Seminarraum (Takustr. 9)

      Comments

      This seminar explores the multifaceted world of startups, providing students with a comprehensive understanding of what it takes to succeed in a dynamic and competitive environment. Topics covered include team composition, market analysis, investment logic, emerging trends (such as AI), and common pitfalls faced by startups.
      Unlike traditional seminars, this course emphasizes practical engagement. Students will work on preparing concise "Impulsvorträge" (short, 15-minute talks) on specific startup-related topics. These presentations will draw from a variety of sources, including:

      * Web Research: Gathering insights from industry reports, blogs, and articles.
      * Interviews: Engaging with actual startups to gain firsthand knowledge and perspectives.
      * Trend Analysis: Examining current innovations and disruptions in the startup ecosystem.


      Each talk will serve as the starting point for an interactive discussion, stimulating deeper understanding and diverse viewpoints among participants.
      This seminar is ideal for students who are curious about entrepreneurship and eager to explore how startups operate, grow, and navigate challenges in today's fast-paced world.

       
    • 19337517 Seminar / Undergraduate Course
      Seminar/Proseminar: Time Series Learning (Manuel Heurich)
      Schedule: Mo 10:00-12:00 (Class starts on: 2025-04-14)
      Location: A7/SR 140 Seminarraum (Hinterhaus) (Arnimallee 7)

      Comments

      This seminar focuses on Machine Learning approaches that specialize in sequential data. Most real-world data is acquired over time. Moreover, most of the available data is not image data. We will discuss works before the Transformer era (e.g., RNNs, LSTMs) and highlight their strengths and weaknesses outside the Computer Vision domain. More recently, transformer-based approaches have outperformed earlier methods. We selectively pick works that highlight their strength in knowledge discovery on sequential data. With the strong trend towards powerful multi-modal models, the seminar aims to introduce state-of-the-art methods to produce robust embeddings based on Time Series data.

  • Software Project Data Science B

    0590bB2.8
    • 19308312 Project Seminar
      Implementation Project: Applications of Algorithms (Mahmoud Elashmawi)
      Schedule: Do 08:30-10:00 (Class starts on: 2025-04-10)
      Location: T9/053 Seminarraum (Takustr. 9)

      Comments

      Contents

      We choose a typical application area of algorithms, usually for geometric problems, and develop software solutions for it, e.g., computer graphics (representation of objects in a computer, projections, hidden edge and surface removal, lighting, raytracing), computer vision (image processing, filtering, projections, camera calibration, stereo-vision) or pattern recognition (classification, searching).

      Prerequsitions

      Basic knowledge in design and anaylsis of algorithms.

      Suggested reading

      je nach Anwendungsgebiet
    • 19315312 Project Seminar
      Software Project: Distributed Systems (Justus Purat)
      Schedule: Mi 12:00-14:00 (Class starts on: 2025-04-16)
      Location: T9/K63 Hardwarepraktikum (Takustr. 9)
    • 19334212 Project Seminar
      Softwareproject: Machine Learning and Explainability for Improved (Cancer) Treatment (Pauline Hiort)
      Schedule: Di 15:00-17:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-02-26)
      Location: T9/K40 Multimediaraum (Takustr. 9)

      Comments

      In the software project, we will implement, train, and evaluate various machine learning (ML) methods. The focus of the project is on neural networks (NN) and their explainability. We will compare the methods with different baseline models, such as regression models. The various ML methods will be applied to a specific dataset, e.g., for predicting drug combinations for cancer treatment, and evaluated accordingly. The dataset will be prepared by us and analyzed using the implemented methods. Additionally, we will focus on explainability to ensure that the predictions of the ML models are understandable and interpretable. For this purpose, we will integrate appropriate explainability techniques to better understand and visualize the decision-making processes of the models.

      The programming language is Python, and we plan to use modern Python modules for ML like scikit-learn, and PyTorch. Good Python skills are required. The goal is to create a Python package that provides reusable code for preprocessing, training ML models, and evaluating results with documentation (e.g., using Sphinx) for the specific use case. The software project takes place throughout the semester and can also be conducted in English.
    • 19337112 Project Seminar
      Softwareproject: Chat, Search and Summaries: Smarter Apps with LLMs (Tim Landgraf)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      In this hands-on project course, students will dive into the cutting-edge world of Large Language Models (LLMs) to design and build smarter applications. Working in teams of 2-3, participants will tackle the challenge of creating applications that leverage LLMs for tasks such as intelligent document search, dynamic user interaction, and content summarization.

      The course spans two months of continuous development and offers an end-to-end exploration of software design. While LLMs form the core of each project, students will focus on integrating and enhancing their capabilities through:

      * Front-End Development: Crafting intuitive user interfaces to interact with the LLM-powered backend.
      * Back-End Development: Architecting robust systems to manage APIs, databases, and application logic.
      * API Design and Integration: Seamlessly connecting LLMs to external systems, ensuring efficient data flow and real-time processing.
      * Scalability and Deployment: Addressing performance and hosting considerations to prepare apps for real-world use.

      Throughout the course, students will engage in collaborative design, iterative development, and peer feedback sessions. By the end, teams will have a fully functional application and a deeper understanding of how LLMs can transform user experiences in modern software solutions.

      This course is ideal for students eager to expand their skills in building innovative software while exploring the exciting potential of LLMs.

       

  • Data Base Systems for students of Data Science

    0590bB2.9
    • 19301501 Lecture
      Database Systems (Agnès Voisard)
      Schedule: Di 14:00-16:00, Do 14:00-16:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-04-15)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Additional information / Pre-requisites

      Requirements

      • ALP 1 - Functional Programming
      • ALP 2 - Object-oriented Programming
      • ALP 3 - Data structures and data abstractions
      • OR Informatik B

      Comments

      Content

      Database design with ERM/ERDD. Theoretical foundations of relational database systems: relational algebra, functional dependencies, normal forms. Relational database development: SQL data definitions, foreign keys and other integrity constraints, SQL as applicable language: essential language elements, embedding in programming language. Application programming; object-relational mapping. Security and protection concepts. Transaction subject, transactional guaranties, synchronization of multi user operations, fault tolerance features. Application and new developments: data warehousing, data mining, OLAP.

      Project: the topics are deepened in an implementation project for student groups.

      Suggested reading

      • Alfons Kemper, Andre Eickler: Datenbanksysteme - Eine Einführung, 5. Auflage, Oldenbourg 2004
      • R. Elmasri, S. Navathe: Grundlagen von Datenbanksystemen, Pearson Studium, 2005
    • 19301502 Practice seminar
      Practice seminar for Database systems (Muhammed-Ugur Karagülle)
      Schedule: Mo 12:00-14:00, Mo 14:00-16:00, Mo 16:00-18:00, Di 08:00-10:00, Di 10:00-12:00, Di 12:00-14:00, Mi 10:00-12:00, Mi 12:00-14:00, Mi 14:00-16:00, Do 08:00-10:00, Do 10:00-12:00, Do 12:00-14:00, Do 16:00-18:00, Fr 10:00-12:00, Fr 14:00-16:00, Fr 16:00-18:00 (Class starts on: 2025-04-14)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

  • Software Project Data Science A

    0590bB2.1
    • 19308312 Project Seminar
      Implementation Project: Applications of Algorithms (Mahmoud Elashmawi)
      Schedule: Do 08:30-10:00 (Class starts on: 2025-04-10)
      Location: T9/053 Seminarraum (Takustr. 9)

      Comments

      Contents

      We choose a typical application area of algorithms, usually for geometric problems, and develop software solutions for it, e.g., computer graphics (representation of objects in a computer, projections, hidden edge and surface removal, lighting, raytracing), computer vision (image processing, filtering, projections, camera calibration, stereo-vision) or pattern recognition (classification, searching).

      Prerequsitions

      Basic knowledge in design and anaylsis of algorithms.

      Suggested reading

      je nach Anwendungsgebiet
    • 19314012 Project Seminar
      Software Project: Semantic Technologies (Adrian Paschke)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-04-16)
      Location: A7/SR 031 (Arnimallee 7)

      Additional information / Pre-requisites

      Corporate Semantic Web

      Further information can be found on the course website

      Comments

      Mixed groups of master and bachelor students will either implement an independent project or are part of a larger project in the area of semantic technologies. They will gain in-depth programming knowledge about applications of semantic technologies and artificial intelligence techniques in the Corporate Semantic Web. They will practice teamwork and best practices in software development of large distributed systems and Semantic Web applications. The software project can be done in collaboration with an external partner from industry or standardization. It is possible to continue the project as bachelor or master thesis.

      Suggested reading

      Corporate Semantic Web
    • 19315312 Project Seminar
      Software Project: Distributed Systems (Justus Purat)
      Schedule: Mi 12:00-14:00 (Class starts on: 2025-04-16)
      Location: T9/K63 Hardwarepraktikum (Takustr. 9)
    • 19334212 Project Seminar
      Softwareproject: Machine Learning and Explainability for Improved (Cancer) Treatment (Pauline Hiort)
      Schedule: Di 15:00-17:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-02-26)
      Location: T9/K40 Multimediaraum (Takustr. 9)

      Comments

      In the software project, we will implement, train, and evaluate various machine learning (ML) methods. The focus of the project is on neural networks (NN) and their explainability. We will compare the methods with different baseline models, such as regression models. The various ML methods will be applied to a specific dataset, e.g., for predicting drug combinations for cancer treatment, and evaluated accordingly. The dataset will be prepared by us and analyzed using the implemented methods. Additionally, we will focus on explainability to ensure that the predictions of the ML models are understandable and interpretable. For this purpose, we will integrate appropriate explainability techniques to better understand and visualize the decision-making processes of the models.

      The programming language is Python, and we plan to use modern Python modules for ML like scikit-learn, and PyTorch. Good Python skills are required. The goal is to create a Python package that provides reusable code for preprocessing, training ML models, and evaluating results with documentation (e.g., using Sphinx) for the specific use case. The software project takes place throughout the semester and can also be conducted in English.
    • 19337112 Project Seminar
      Softwareproject: Chat, Search and Summaries: Smarter Apps with LLMs (Tim Landgraf)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-04-17)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      In this hands-on project course, students will dive into the cutting-edge world of Large Language Models (LLMs) to design and build smarter applications. Working in teams of 2-3, participants will tackle the challenge of creating applications that leverage LLMs for tasks such as intelligent document search, dynamic user interaction, and content summarization.

      The course spans two months of continuous development and offers an end-to-end exploration of software design. While LLMs form the core of each project, students will focus on integrating and enhancing their capabilities through:

      * Front-End Development: Crafting intuitive user interfaces to interact with the LLM-powered backend.
      * Back-End Development: Architecting robust systems to manage APIs, databases, and application logic.
      * API Design and Integration: Seamlessly connecting LLMs to external systems, ensuring efficient data flow and real-time processing.
      * Scalability and Deployment: Addressing performance and hosting considerations to prepare apps for real-world use.

      Throughout the course, students will engage in collaborative design, iterative development, and peer feedback sessions. By the end, teams will have a fully functional application and a deeper understanding of how LLMs can transform user experiences in modern software solutions.

      This course is ideal for students eager to expand their skills in building innovative software while exploring the exciting potential of LLMs.

       

  • Modules w/o courses in this semester

    16 Modules
    • Introduction to Profile Areas 0590bA1.1
    • Statistics for Students of Data Science 0590bA1.2
    • Machine Learning for Data Science 0590bA1.3
    • Programming for Data Science 0590bA1.4
    • Pattern Recognition 0089cA1.12
    • Network-Based Information Systems 0089cA1.13
    • Computer Security 0089cA1.16
    • Distributed Systems 0089cA1.20
    • Advanced Topics in Data Management 0089cA1.29
    • Artificial Intelligence 0089cA1.9
    • Advanced Algorithms 0089cA2.1
    • Telematics 0089cA3.5
    • Big Data Analysis in Bioinformatics 0262cD1.8
    • Research Practice 0590bB1.2
    • Interdisciplinary Apporaches (Data Science) B 0590bB1.28
    • Accompanying colloquium 0590bE1.2