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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
    • 19304201 Lecture
      Machine Learning (Tim Landgraf)
      Schedule: Mi 12:00-14:00, Do 14:00-16:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-10-15)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Additional information / Pre-requisites

      Prerequisites: Basic knowledge  in Mathematics and Algorithms and Data structures.

      Comments

      Contents: Bayesian methods of pattern recognition, clustering, expectation maximization, neuronal networks and learning algorithms, associate networks, recurrent networks. Computer-vision with neuronal networks, applications in Robotics.

      Suggested reading

      wird noch bekannt gegeben
    • 19304202 Practice seminar
      Practice seminar for Pattern recognition / Machine Learning (Manuel Heurich)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-10-20)
      Location: T9/SR 005 Übungsraum (Takustr. 9)

  • Programming for Data Science

    0590aA1.4
    • 19330313 Lab Seminar
      Programming for Data Science (Sandro Andreotti)
      Schedule: Di 12:00-16:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-10-14)
      Location: KöLu24-26/SR 006 Neuro/Mathe (Königin-Luise-Str. 24 / 26)

      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.

  • Data Base Systems for Students of Data Science

    0590aB1.20
    • 19301501 Lecture
      Database Systems (Katharina Baum)
      Schedule: Di 10:00-12:00, Do 10:00-12:00 (Class starts on: 2025-10-14)
      Location: T9/SR 005 Übungsraum (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 (Pascal Iversen)
      Schedule: Mi 12:00-14:00 (Class starts on: 2025-10-15)
      Location: T9/049 Seminarraum (Takustr. 9)

  • Telematics

    0590aB1.23
    • 19305101 Lecture
      Telematics (Jochen Schiller)
      Schedule: Mo 14:00-16:00, Fr 14:00-16:00 (Class starts on: 2025-10-13)
      Location: T9/051 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

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

       

      Comments

      Content

      Telematics = telecommunications + informatics (often also called computer networks) covers a wide spectrum of topics - from communication engineering to the WWW and advanced applications.

      The lecture addresses topics such as:

      • Basic background: protocols, 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 (QUIC etc.).

      At the End of this course, you should...

      • know how networks in general are organized
      • know what the Internet could be or is
      • understand how wired/wireless (see Mobile Communications) networks work
      • understand why/how protocols and layers are used
      • understand how e-mails, videos get to where you are
      • understand how operators operate real, big networks
      • understand the cooperation of web browsers with web servers
      • be aware of security issues when you use the network
      • be familiar with acronyms like: ALOHA, ARP, ATM, BGP, CDMA, CDN, CIDR, CSMA, DCCP, DHCP, ETSI, FDM, FDMA, FTP, HDLC, HTTP, ICMP, ICN, IEEE, IETF, IP, IMAP, ISP, ITU, ISO/OSI, LAN, LTE, MAC, MAN, MPLS, MTU, NAT, NTP, PCM, POTS, PPP, PSTN, P2P, QUIC, RARP, SCTP, SMTP, SNMP, TCP, TDM, TDMA, UDP, UMTS, VPN, WAN, ...

      Literature

      • A. Tanenbaum & D. Wetherall: Computer Networks (5th edition)
      • J. Kurose & K. Ross: Computer Networking (6th edition)
      • S. Keshav: Mathematical Foundations of Computer Networking (2012)
      • W. Stallings book, W. Goralski book 
      • IETF drafts and RFCs
      • IEEE 802 LAN/MAN standards

      Prerequisites

      As this is a Master Course you have to know the basics of computer networks already (e.g. from the OS&CN BSc course or any other basic networking course). That means you know what protocol stacks are, know the basic ideas behind TCP/IP, know layering principles, got a rough understanding of how the Internet works. This course will recap the basics but then proceed to the more advanced stuff.

      Resources & Organization

      The course comprises about 30 "lectures", 90 minutes each, following the inverted or flipped classroom principle. I.e. you will be able to access a video of the lecture before we discuss the content in class. To be able to discuss you have to watch the video BEFORE we meet! This is your main assignment - go through the video, prepare questions if something is not clear. During the meetings there will be a recap of the main ideas plus enough time to discuss each topic if necessary.

      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 (Jochen Schiller, Marius Max Wawerek)
      Schedule: Mo 16:00-18:00 (Class starts on: 2025-10-20)
      Location: T9/055 Seminarraum (Takustr. 9)

  • Advanced Analysis

    0590aB1.24
    • 19303501 Lecture
      Advanced Algorithms (N.N.)
      Schedule: Mo 10:00-12:00, Fr 10:00-12:00 (Class starts on: 2025-10-13)
      Location: KöLu24-26/SR 006 Neuro/Mathe (Königin-Luise-Str. 24 / 26)

      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

      The class focuses on topics such as

      • general principles of algorithm design,
      • network flows,
      • number-theoretic algorithms (including the RSA crypto system),
      • string matching,
      • NP-completeness,
      • approximation algorithms for hard problems,
      • arithmetic algorithms and circuits, fast fourier transform.

      Suggested reading

      • Cormen, Leiserson, Rivest, Stein: Introduction to Algorithms, 2nd Ed. McGraw-Hill 2001
      • Kleinberg, Tardos: Algorithm Design Addison-Wesley 2005.
    • 19303502 Practice seminar
      Practice seminar for Advanced Algorithms (N.N.)
      Schedule: Mi 08:00-10:00, Mi 14:00-16:00 (Class starts on: 2025-10-15)
      Location: T9/046 Seminarraum (Takustr. 9)

  • Pattern Recognition

    0590aB1.26
    • 19304201 Lecture
      Machine Learning (Tim Landgraf)
      Schedule: Mi 12:00-14:00, Do 14:00-16:00, zusätzliche Termine siehe LV-Details (Class starts on: 2025-10-15)
      Location: T9/Gr. Hörsaal (Takustr. 9)

      Additional information / Pre-requisites

      Prerequisites: Basic knowledge  in Mathematics and Algorithms and Data structures.

      Comments

      Contents: Bayesian methods of pattern recognition, clustering, expectation maximization, neuronal networks and learning algorithms, associate networks, recurrent networks. Computer-vision with neuronal networks, applications in Robotics.

      Suggested reading

      wird noch bekannt gegeben
    • 19304202 Practice seminar
      Practice seminar for Pattern recognition / Machine Learning (Manuel Heurich)
      Schedule: Mo 14:00-16:00 (Class starts on: 2025-10-20)
      Location: T9/SR 005 Übungsraum (Takustr. 9)

  • Special Aspects of Data Administration

    0590aB1.29
    • 19304801 Lecture
      Geospatial Databases (Agnès Voisard)
      Schedule: Di 14:00-16:00 (Class starts on: 2025-10-14)
      Location: T9/046 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: 2025-10-16)
      Location: A7/SR 031 (Arnimallee 7)

  • Special Aspects of Data Science in Life Sciences

    0590aB2.4
    • 19328301 Lecture
      Data Visualization (Claudia Müller-Birn)
      Schedule: Di 12:00-14:00 (Class starts on: 2025-10-14)
      Location: T9/SR 006 Seminarraum (Takustr. 9)

      Additional information / Pre-requisites

      Link to the course on the HCC-Website: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2025_26/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

      Textbook

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

      Additional Literature

      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.
    • 60101901 Lecture
      Advanced Biometrical Methods (Frank Konietschke)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-10-15)
      Location: A6/SR 032 Seminarraum (Arnimallee 6)

      Comments

      This course will introduce advanced biometric methods used in clinical and observational studies. Topics covered include complex study designs and advanced modeling. Students should have a solid background in statistics and an interest in medical applications of statistics.
    • 19328302 Practice seminar
      Data Visualization (Malte Heiser)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-10-16)
      Location: T9/053 Seminarraum (Takustr. 9)
    • 60101902 Practice seminar
      Practice seminar for Advanced Biometrical Methods (Frank Konietschke)
      Schedule: Mi 16:00-18:00 (Class starts on: 2025-10-15)
      Location: A6/SR 032 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: 2025-10-13)
      Location: T9/046 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
      Kryptowährungen und Blockchain (Katinka Wolter)
      Schedule: Di 12:00-14:00 (Class starts on: 2025-10-14)
      Location: , T9/051 Seminarraum

      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: 2025-10-13)
      Location: T9/046 Seminarraum (Takustr. 9)
    • 19328602 Practice seminar
      Practice Session on Cryptocurrencies (Justus Purat)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-10-16)
      Location: T9/051 Seminarraum (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: 2025-10-13)
      Location: T9/046 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
      Kryptowährungen und Blockchain (Katinka Wolter)
      Schedule: Di 12:00-14:00 (Class starts on: 2025-10-14)
      Location: , T9/051 Seminarraum

      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: 2025-10-13)
      Location: T9/046 Seminarraum (Takustr. 9)
    • 19328602 Practice seminar
      Practice Session on Cryptocurrencies (Justus Purat)
      Schedule: Do 10:00-12:00 (Class starts on: 2025-10-16)
      Location: T9/051 Seminarraum (Takustr. 9)

  • Selected Topics in Data Science Technologies

    0590aB3.5
    • 19312101 Lecture
      Systems Software (Barry Linnert)
      Schedule: Di 12:00-14:00, Mi 12:00-14:00 (Class starts on: 2025-10-14)
      Location: A7/SR 031 (Arnimallee 7)

      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 as well as in English.

      Homepage

      https://www.inf.fu-berlin.de/w/SE/VorlesungBetriebssysteme2025

      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
    • 19312102 Practice seminar
      Practice seminar for Systems Software (Barry Linnert)
      Schedule: Do 14:00-16:00 (Class starts on: 2025-10-16)
      Location: T9/046 Seminarraum (Takustr. 9)

  • Software Project Data Science

    0590aB3.1
    • 19308312 Project Seminar
      Implementation Project: Applications of Algorithms (Günther Rothe)
      Schedule: Di 08:00-10:00 (Class starts on: 2025-10-14)
      Location: T9/SR 006 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
    • 19309212 Project Seminar
      SWP: Smart Home Demo Lab (Jochen Schiller, Marius Max Wawerek)
      Schedule: Mi 10:00-12:00 (Class starts on: 2025-10-15)
      Location: T9/K63 Hardwarepraktikum (Takustr. 9)

      Additional information / Pre-requisites

      In this course you will be expected to write code. The outcome of your software project should be a concrete contribution to the RIOT code base, and take the shape of one or more pull request(s) to the RIOT github (https://github.com/RIOT-OS/RIOT). Before you start coding, refer to the starting guide

      https://github.com/RIOT-OS/RIOT/wiki#wiki-start-the-riot

      Comments

      Softwareproject Smart Home Demo Lab

      In this course, students will work on topics related to the Smart Home Demo Lab of the Computer Systems & Telematics working group.

      The topics include:

      • Creation of a Smart Home ecosystem
      • Machine Learning (ML) based analysis of Smart Home datasets
      • Experiments with and Improvements of existing ML models
      • Design of Smart Home Usage scenarios
      • Development of your own (virtual) IoT device

      Participants will work in smaller groups (3-5 students), where each group will focus on a specific topic.

      Regarding Organization: The software project will take course throughout the whole lecture period. First a kick off meeting with all participants will be held. There all the different topics will be presented. Afterwards each student will hand in a list of topic priorities.

      The actual work on the topics will occur in multiple two week sprints. Finally at the end of the lecture period one overall final presentation will be held showing the results of all topics.

      Depending on the needs of the students the software project can be held in either German or English.

      Suggested reading

      • A. S. Tanenbaum, Modern Operating Systems, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall Press, 2007.
      • Shelby, Zach, and Carsten Bormann. 6LoWPAN: The wireless embedded Internet. Vol. 43. Wiley. com, 2011.
      • A. Dunkels, B. Gronvall, and T. Voigt, "Contiki - a lightweight and flexible operating system for tiny networked sensors." in LCN. IEEE Computer Society, 2004, pp. 455-462.
      • P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, D. Gay, J. Hill, M. Welsh, E. Brewer, and D. Culler, "TinyOS: An Operating System for Sensor Networks," in Ambient Intelligence, W. Weber, J. M. Rabaey, and E. Aarts, Eds. Berlin/Heidelberg: Springer-Verlag, 2005, ch. 7, pp. 115-148.
      • Oliver Hahm, Emmanuel Baccelli, Mesut Günes, Matthias Wählisch, Thomas C. Schmidt, "RIOT OS: Towards an OS for the Internet of Things," in Proceedings of the 32nd IEEE International Conference on Computer Communications (INFOCOM), Poster Session, April 2013.
      • M.R. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L.A. Grieco, G. Boggia and M. Dohler, "Standardized Protocol Stack For The Internet Of (Important) Things", IEEE Communications Surveys and Tutorials, December 2012.
      • J. Wiegelmann, Softwareentwicklung in C für Mikroprozessoren und Mikrocontroller, Hüthig, 2009
    • 19314012 Project Seminar
      Software Project: Semantic Technologies (Adrian Paschke)
      Schedule: Mi 14:00-16:00 (Class starts on: 2025-10-15)
      Location: A3/SR 115 (Arnimallee 3-5)

      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
    • 19332512 Project Seminar
      Softwareprojekt: Applying LLMs in Healthcare (Malte Heiser)
      Schedule: Di 10:00-12:00 (Class starts on: 2025-10-14)
      Location: , Virtueller Raum 35

      Additional information / Pre-requisites

      The seminar will take place at Königin-Luise-Straße 24/26, room 111.

      Link to the software project on the HCC-Website: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2025_26/swp_applying_llms_in_healthcare.html

      Comments

      In this software project, students collaboratively develop an application based on a Large Language Model (LLM) for patients in the context of an emergency department. The core focus is on enabling patients to feel emotionally informed while they wait, with the goal of empower them to reflect on their symptoms independently.   This real-world problem is used as a foundation to build a functional LLM-based application while fostering interdisciplinary thinking, technical creativity, and the ability to work effectively in agile teams. The project is structured around the Scrum framework and offers students the opportunity to gain practical development experience. Students apply agile principles to organize the development process iteratively and collaboratively — from requirements analysis through planning and implementation to final reflection.   This allows them to strengthen their communication skills, tackle problems and tasks in a complex environment, and advance their technical competencies. Weekly sessions throughout the semester provide a space for students to shape the process and discuss their progress. We are available as advisors and mentors to support them and provide all necessary methods and competencies as needed.

      Suggested reading

      Literature, materials and equipment will be provided during the event.
    • 19334212 Project Seminar
      Software Project: Machine Learning for data from the life sciences (Pascal Iversen, Katharina Baum)
      Schedule: Di 16:00-18:00 (Class starts on: 2025-10-14)
      Location: T9/046 Seminarraum (Takustr. 9)

      Comments

      In this software project, we will work with various ML-based methods for predictions for specific questions from biology, such as predicting the effect of drugs or the development of infection numbers. The focus of the project is explicitly on the development, implementation and evaluation of the methodological framework and less on the preparation of the data.

      The programming language is Python, and we plan to use modern Python modules for ML such as PyTorch or possibly JAX. Good knowledge of Python is a prerequisite. The software project takes place during the semester and can also be carried out in English.

  • Modules w/o courses in this semester

    23 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
    • 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