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 Data Science  
 Course

Data Science

Data Science

0590a_MA120

  • Introduction to Profile Areas

    0590aA1.1

  • Statistics for Students of Data Science

    0590aA1.2

  • Machine Learning for Data Science

    0590aA1.3
    • 19330101 Lecture
      Machine Learning for Data Science (Grégoire Montavon)
      Schedule: Di 16:00-18:00, Do 16:00-18:00 (Class starts on: 2024-10-15)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Comments

      Qualifikationsziele:

      The course provides an overview of machine learning methods and algorithms for different learning tasks, namely supervised, unsupervised and reinforcement learning.

      In the first part of the course, for each task the main algorithms and techniques will be covered including experimentation and evaluation aspects.

      In the second part of the course, we will focus on specific learning challenges including high-dimensionality, non-stationarity, label-scarcity and class-imbalance.

      By the end of the course, you will have learned how to build machine learning models for different problems, how to properly evaluate their performance and how to tackle specific learning challenges.

      Inhalte

       Es werden Themen aus folgenden Gebieten behandelt:

       

      • Experiment Design
      • Sampling Techniques
      • Data cleansing
      • Storage of large data sets
      • Data visualization and graphs
      • Probabilistic data analysis
      • Prediction methods
      • Knowledge discovery
      • Neural networks
      • Support vector machines
      • Reinforcement learning and agent models
    • 19330102 Practice seminar
      Practice Seminar Machine Learning DatSci (Grégoire Montavon)
      Schedule: Mi 16:00-18:00 (Class starts on: 2024-10-16)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

  • Programming for Data Science

    0590aA1.4
    • 19330313 Lab Seminar
      Programming for Data Science (Sandro Andreotti)
      Schedule: Mo 12:00-16:00 (Class starts on: 2024-10-28)
      Location: A3/SR 120 (Arnimallee 3-5)

      Comments

      Qualifikationsziele

      Die Studentinnen und Studenten haben ein tieferes Verständnis für Konzepte in der Programmierung mit einer höheren Programmiersprache (z. B. C/C++, Java oder Python).

      Inhalte:

      Einführung in verschiedene Arten von Programmiertechniken.

  • Telematics

    0590aB1.23
    • 19305101 Lecture
      Telematics (Jochen Schiller)
      Schedule: Mo 12:00-14:00, Mi 10:00-12:00 (Class starts on: 2024-10-14)
      Location: T9/046 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Requirements: Basic understanding of computer networks, e.g., TI-III

       

      Comments

      This course addresses communication asp. The lecture addresses topics such as:

      • Basic background: protocls, services, models, communication standards;
      • Principles of communication engineering: signals, coding, modulation, media;
      • Data link layer: media access etc.;
      • Local networks: IEEE-Standards, Ethernet, bridges;
      • Network layer: routing and forwarding, Internet protocols (IPv4, IPv6);
      • Transport layer: quality of service, flow control, congestion control, TCP;
      • Internet: TCP/IP protocol suite;
      • Applications: WWW, security, network management;
      • New network concepts.

      In the supplementary exercise course the students will practically apply their knowledge.

      Suggested reading

      • Larry Peterson, Bruce S. Davie: Computernetze - Ein modernes Lehrbuch, dpunkt Verlag, Heidelberg, 2000
      • Krüger, G., Reschke, D.: Lehr- und Übungsbuch Telematik, Fachbuchverlag Leipzig, 2000
      • Kurose, J. F., Ross, K. W.: Computer Networking: A Top-Down Approach Featuring the Internet, Addi-son-Wesley Publishing Company, Wokingham, England, 2001
      • Siegmund, G.: Technik der Netze, 4. Auflage, Hüthig Verlag, Heidelberg, 1999
      • Halsall, F.: Data Communi-cations, Computer Networks and Open Systems 4. Auflage, Addison-Wesley Publishing Company, Wokingham, England, 1996
      • Tanenbaum, A. S.: Computer Networks, 3. Auflage, Prentice Hall, Inc., New Jersey, 1996
    • 19305102 Practice seminar
      Practice seminar for Telematics (Marius Max Wawerek)
      Schedule: Mo 16:00-18:00 (Class starts on: 2024-10-14)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

  • Advanced Analysis

    0590aB1.24
    • 19303501 Lecture
      Advanced Algorithms (László Kozma)
      Schedule: Di 10:00-12:00, Fr 10:00-12:00 (Class starts on: 2024-10-15)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Target audience

      All Master and Bachelor students who are interested in algorithms.

      Prerequisites

      Basic familiarity with the design and analysis of algorithms.

      Comments

      This course will focus on the design and analysis of algorithms, with topics including:

      • general principles of algorithm design,
      • randomized algorithms,
      • dynamic programming,
      • flow problems on graphs,
      • amortized analysis and advanced data structures,
      • theory of NP-completeness,
      • approximation methods for hard problems,
      • other topics.

      Prerequisites are basic knowledge of algorithms and relevant mathematics. All Bachelor and Master students interested in advanced algorithmic techniques are welcome. Lectures are in English.

      Suggested reading

      • Cormen, Leiserson, Rivest, Stein: Introduction to Algorithms, 4th Ed. MIT Press 2022
      • Kleinberg, Tardos: Algorithm Design, Addison-Wesley 2005.
      • Sedgewick, Wayne: Algorithms, 4th Ed., Addison-Wesley 2016
    • 19303502 Practice seminar
      Practice seminar for Advanced Algorithms (László Kozma)
      Schedule: Fr 08:00-10:00, Fr 14:00-16:00 (Class starts on: 2024-10-18)
      Location: T9/046 Seminarraum (Takustr. 9)

  • Pattern Recognition

    0590aB1.26
    • 19315501 Lecture
      Computer Vision (Tim Landgraf)
      Schedule: Mi 10:00-12:00 (Class starts on: 2024-10-16)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

      Comments

      Contents:

      The most frequent tasks in Computer Vision are object (or event) detection and object tracking. In contrast to the field of image processing we often work on a sequence of images (a.k.a. video). In the lecture we will review a number of essential landmark publications and learn about cutting edge technologies of today.
    • 19315502 Practice seminar
      Practice seminar for Computer Vision (Tim Landgraf)
      Schedule: Di 14:00-16:00 (Class starts on: 2024-10-15)
      Location: T9/049 Seminarraum (Takustr. 9)

  • Special Aspects of Data Administration

    0590aB1.29
    • 19304801 Lecture
      Geospatial Databases (Agnès Voisard)
      Schedule: Di 14:00-16:00 (Class starts on: 2024-10-15)
      Location: T9/055 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Zielgruppe:

      Studierende im Masterstudiengang Voraussetzungen: Vorlesung: Einf. in Datenbanksysteme

      Comments

      The goal of this course is to acquire the background of spatial databases, the kernel of Geographic Systems. The major aspects that will be handled are: modeling and querying geospatial information, spatial access methods (SAMs), data representation, basic operations (mostly from computational geometry), and optimization. Insights into current applications such as location-based services (e.g., navigation systems) will also be given. Knowledge in databases is necessary. This course encompasses: formal lectures, exercises, as well as a practical project with PostGIS.
       

      Suggested reading

      Handouts are enough to understand the course.

      The following book will be mostly used: P. Rigaux, M. Scholl, A. Voisard.Spatial Databases - With Application to GIS. Morgan Kaufmann, May 2001. 432 p. (copies in the main library)
    • 19304802 Practice seminar
      Practice seminar for Geospatial Databases (Agnès Voisard)
      Schedule: Do 14:00-16:00 (Class starts on: 2024-10-17)
      Location: T9/K 040 Multimediaraum (Takustr. 9)

  • Special Aspects of Data Science in Life Sciences

    0590aB2.4
    • 19328301 Lecture
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe

      Additional information / Pre-requisites

      https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2021_22/course_data_visualization.html

      Comments

      The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations of data. This course aims to familiarize students with the principles, techniques, and methods in data visualization and provide practical skills for designing and implementing data visualizations.

      This course gives students a solid introduction to the fundamentals of data visualization with current insights from research and practice. By the end of the course, students will

      1. Be able to select and apply methods for designing visualizations based on a problem,
      2. know essential theoretical basics of visualization for graphical perception and cognition,
      3. know and be able to select visualization approaches and their advantages and disadvantages,
      4. be able to evaluate visualization solutions critically, and
      5. have acquired practical skills for implementing visualizations.

      This course is intended for students interested in using data visualization in their work and students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

      In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

      Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

      Suggested reading

      Textbuch

      Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

       

      Zusätzliche Literatur

      Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

      Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

      Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.
    • 60102001 Lecture
      Methods for clinical trials (N.N.)
      Schedule: Fr 14:00-16:00 (Class starts on: 2024-10-18)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

      Comments

      In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.
    • 60102301 Lecture
      Statistical Methods for Small Sample Sizes (Frank Konietschke)
      Schedule: Mi 14:00-16:00 (Class starts on: 2024-10-16)
      Location: A6/SR 031 Seminarraum (Arnimallee 6)

      Comments

      In this course, we introduce and discuss statistical inference methods for analyzing trials with small sample sizes. We hereby explore the impact of the standard assumption “N is large” and try to find an answer to the question “what means large?” The inference methods will cover estimation of treatment effects, confidence interval computations and hypothesis testing in both parametric and nonparametric models. Rank tests, bootstrap and permutation methods will be investigated in detail as approximation methods. This class aspires to learn about modern statistical tools that were designed to make accurate conclusions when sample sizes are rather small.

       
    • 19328302 Practice seminar
      Data Visualization (Claudia Müller-Birn)
      Schedule: -
      Location: keine Angabe
    • 60102002 Practice seminar
      Practice seminar for Methods for clinical trials (N.N.)
      Schedule: Fr 16:00-18:00 (Class starts on: 2024-10-18)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)
    • 60102302 Practice seminar
      Practice seminar for Statistical Methods for Small Sample Sizes (Frank Konietschke)
      Schedule: Mi 16:00-18:00 (Class starts on: 2024-10-16)
      Location: A6/SR 031 Seminarraum (Arnimallee 6)

  • Special Aspects of Data Science Technologies

    0590aB3.3
    • 19327201 Lecture
      Data compression (Heiko Schwarz)
      Schedule: Mo 14:00-16:00 (Class starts on: 2024-10-14)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

      In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

      The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

      • Unique decodability and prefix codes
      • Entropy and entropy rate as theoretical limits of lossless compression
      • Optimal codes, Huffman codes
      • Arithmetic coding
      • Lempel-Ziv coding
      • Linear prediction
      • Examples from text, image and audio compression

      In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

      • Scalar quantization, optimal scalar quantization
      • Theoretical limits of lossy compression: Rate distortion functions
      • Vector quantization
      • Predictive quantization
      • Transform coding
      • Examples from audio, image, and video compression

      Suggested reading

      • Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
      • Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
      • Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
      • Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
      • Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.
    • 19328601 Lecture
      Cryptocurrencies and Blockchain (Katinka Wolter, Justus Purat)
      Schedule: Di 10:00-12:00 (Class starts on: 2024-10-15)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      We will study the history, technology and applications of cryptocurrencies and blockchain.

      Suggested reading

      Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder
    • 19327202 Practice seminar
      Practice seminar for Data Compression (Heiko Schwarz)
      Schedule: Mo 12:00-14:00 (Class starts on: 2024-10-14)
      Location: T9/049 Seminarraum (Takustr. 9)
    • 19328602 Practice seminar
      Practice Session on Cryptocurrencies (Justus Purat)
      Schedule: Do 10:00-12:00, zusätzliche Termine siehe LV-Details (Class starts on: 2024-09-27)
      Location: T9/SR 005 Übungsraum (Takustr. 9)

  • Current Research Topics in Data Science Technologies

    0590aB3.4
    • 19327201 Lecture
      Data compression (Heiko Schwarz)
      Schedule: Mo 14:00-16:00 (Class starts on: 2024-10-14)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

      In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

      The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

      • Unique decodability and prefix codes
      • Entropy and entropy rate as theoretical limits of lossless compression
      • Optimal codes, Huffman codes
      • Arithmetic coding
      • Lempel-Ziv coding
      • Linear prediction
      • Examples from text, image and audio compression

      In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

      • Scalar quantization, optimal scalar quantization
      • Theoretical limits of lossy compression: Rate distortion functions
      • Vector quantization
      • Predictive quantization
      • Transform coding
      • Examples from audio, image, and video compression

      Suggested reading

      • Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
      • Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
      • Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
      • Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
      • Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.
    • 19328601 Lecture
      Cryptocurrencies and Blockchain (Katinka Wolter, Justus Purat)
      Schedule: Di 10:00-12:00 (Class starts on: 2024-10-15)
      Location: T9/049 Seminarraum (Takustr. 9)

      Comments

      We will study the history, technology and applications of cryptocurrencies and blockchain.

      Suggested reading

      Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder
    • 19327202 Practice seminar
      Practice seminar for Data Compression (Heiko Schwarz)
      Schedule: Mo 12:00-14:00 (Class starts on: 2024-10-14)
      Location: T9/049 Seminarraum (Takustr. 9)
    • 19328602 Practice seminar
      Practice Session on Cryptocurrencies (Justus Purat)
      Schedule: Do 10:00-12:00, zusätzliche Termine siehe LV-Details (Class starts on: 2024-09-27)
      Location: T9/SR 005 Übungsraum (Takustr. 9)

  • Selected Topics in Data Science Technologies

    0590aB3.5
    • 19312101 Lecture
      Systems Software (Barry Linnert)
      Schedule: Mo 10:00-12:00, Do 12:00-14:00 (Class starts on: 2024-10-14)
      Location: T9/049 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Language

      The course language is German as is the oral presentation of the lecturer, but the slides and all written material is available in English. You can always ask questions in English. The practice sheets and final exam are formulated in German, but may be answered in English, too.

      Homepage

      https://www.inf.fu-berlin.de/w/SE/VorlesungBetriebssysteme

      Comments

      Operating systems tie together the execution of applications, user experience and usability with the management of computer hardware. Starting with the tasks an operating system has to perform and the requirements it has to meet, the most important aspects of design and development of modern operating systems will be introduced:

      • Structure and design of an operating system including historical summary, structures and philosophies of OS design and resources and resource management
      • Threads and processes including thread management
      • Scheduling including real-time scheduling
      • Process interaction and inter-process communication
      • Resource management including device operation, driver development, management and operation of input- and output devices
      • Memory management including address spaces and virtual memory
      • File system including management and operation of discs and memory hierarchy
      • Distributed operating systems including distributed architectures for resource management
      • Performance evaluation and modeling including overload detection and handling

      Modern operating systems provide examples for different aspects and current research will be introduced. The tutorials serve to reflect the topics dealt with in the lecture and to acquire experience by developing a small operating system.

      Suggested reading

      • A.S. Tanenbaum: Modern Operating Systems, 2nd Ed. Prentice-Hall, 2001
      • A. Silberschatz et al.: Operating Systems Concepts with Java, 6th Ed. Wiley, 2004
    • 19315401 Lecture
      Graph and Network Algorithms (Günther Rothe)
      Schedule: Mo 14:00-16:00, Fr 12:00-14:00 (Class starts on: 2024-10-14)
      Location: T9/SR 005 Übungsraum (Takustr. 9)

      Additional information / Pre-requisites

      Target Audience

      Masters students in Computer Science or Mathematics, advanced Bachelor students.

      Prerequisites

      "Advanced Algorithms" or a similar class

      Comments

      Graphs and networks are an important modeling tool for all kinds of relations in Computer Science and beyond, for example social networks, traffic networks, and so on. We will treat algorithmic problems that arise in this context:

      • analysis of networks
      • optimization in graphs
      • graph drawing

      Suggested reading

      Wird noch bekannt gegeben.
    • 19312102 Practice seminar
      Practice seminar for Systems Software (Barry Linnert)
      Schedule: Mi 10:00-12:00 (Class starts on: 2024-10-16)
      Location: T9/049 Seminarraum (Takustr. 9)
    • 19315402 Practice seminar
      Practice seminar for Graph and Network Algorithms (Mahmoud Elashmawi, Günther Rothe)
      Schedule: Mi 12:00-14:00 (Class starts on: 2024-10-16)
      Location: T9/053 Seminarraum (Takustr. 9)

  • Software Project Data Science

    0590aB3.1
    • 19308312 Project Seminar
      Implementation Project: Applications of Algorithms (László Kozma)
      Schedule: Di 14:00-16:00 (Class starts on: 2024-10-08)
      Location: T9/K 040 Multimediaraum (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: 2024-10-16)
      Location: A7/SR 031 (Arnimallee 7)

      Additional information / Pre-requisites

      Further information can be found on the course website of the AG Corporate Semantic Web.

      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 AI 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 AI 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.

  • Modules w/o courses in this semester

    24 Modules
    • Data Science in the Social Sciences 0590aB1.1
    • Mobile Mental Health 0590aB1.10
    • Developing Psychological Online Interventions 0590aB1.11
    • Selected Topics in Data Science in the Social Sciences 0590aB1.12
    • Special Aspects of Data Science in the Social Sciences 0590aB1.13
    • Ethical Foundations of Data Science 0590aB1.2
    • Data Base Systems for Students of Data Science 0590aB1.20
    • Distributed Systems 0590aB1.21
    • Mobile Communications 0590aB1.22
    • Computer Security 0590aB1.25
    • Network-Based Information Systems 0590aB1.27
    • Artificial Intelligence 0590aB1.28
    • Research Practice 0590aB1.3
    • Machine Learning in Bioinformatics 0590aB1.30
    • Big Data Analysis in Bioinformatics 0590aB1.31
    • Complex Systems in Bioinformatics 0590aB1.32
    • Neurocognitive Methods and Programming for Data Science 0590aB1.4
    • Cognitive Neuroscience for Data Science A 0590aB1.5
    • Cognitive Neuroscience for Data Science B 0590aB1.6
    • Differential Psychological Approaches in Data Sciences 0590aB1.7
    • Natural Language Processing 0590aB1.8
    • Introduction to Psychoinformatics 0590aB1.9
    • Data Science in the Life Sciences 0590aB2.1
    • Selected Topics in Data Science in Life Sciences 0590aB2.5