Contents: 

Introduction to Functional Programming (Haskell): 

• Syntax (Backus-Naur Form) 
• primitive data types, lists, tuples, strings 
• • expressions, function definitions, recursion and iteration  evaluation strategies of functional programs
  • higher-order functions
  • polymorphism 
  • type system and verification 
• Algebraic and abstract data types 
• input, output and Monads
• searching and sorting algorithms 

Introduction of computability: 

• Lambda calculus 
• Primitive Recursion 
• μ-Recursion 

Proofs of program properties: 

• Term Rewriting 
• Structural induction 
• Termination 

Implementation and programming technique: 

• Modular program design

• Simon Thompson: Haskell: The Craft of Functional Programming, 2nd Edition, Addison-Wesley, 1999
• Graham Hutton: Programming in Haskell, Cambridge University Press, 2007
• Bird, R./Wadler, Ph.: Einführung in Funktionale Programmierung, Hanser Verlag, 1982
• Hans Hermes: Aufzählbarkeit, Entscheidbarkeit, Berechenbarkeit, Springer-Verlag 1978

Tutorien finden erst ab der 2. Vorlesungswoche statt

• Analysis of sorting algorithms: Mergesort, Quicksort, etc.
• ADTs priority queue and dictionary and corresponding data structures: heaps, hashing, binary search trees, B-trees, etc. 
• Graph algorithms: breadth first search and depth first search, topolocigal sorting, minimum spanning trees, shortest paths.
• Analysis on Strings.
• Memory-management.
• Algorithm design: divide-and-conquer, greedy algorithms, dynamic programming.
• Mathematical analysis of algorithms: running time, space.

• P. Morin: Open Data Structures, an open content textboox.
• T. H. Cormen, C. Leiserson, R. Rivest, C. Stein: Introduction to Algorithms, MIT Press, 2009.
• R. Sedgewick: Algorithms in Java (Part 1–5), Addison-Wesley, 2003.
• G. Saake, S. Sattler: Algorithmen und Datenstrukturen, dpunkt.verlag, 2013.
• M. Dietzfelbinger, K. Mehlhorn, P. Sanders. Algorithmen und Datenstrukturen: Die Grundwerkzeuge, Springer, 2014.
• M.T. Goodrich, R. Tamassia: Data Structures and Algorithms in Java, Wiley, 2014.

The module Computer Architecture covers basic concepts of computer systems. Topics are von-Neuman/Harvard architecture, microarchitectures, RISC/CISC, micro programming, pipelining, caches, memory hierarchy, bus systems, assembler programming, multi processor systems, branch prediction, representation of numbers and other data types, computer arithmetic.

• Andrew S. Tannenbaum: Computerarchitektur, 5.Auflage, Pearson Studium, 2006
• English: Andrew S. Tanenbaum (with contributions from James R. Goodman):
• Structured Computer Organization, 4th Ed., Prentice Hall International, 2005.

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.

• A.S. Tanenbaum: Modern Operating Systems, 2nd Ed. Prentice-Hall, 2001
• A. Silberschatz et al.: Operating Systems Concepts with Java, 6th Ed. Wiley, 2004

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.

• 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

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

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

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

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

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

Wechselnd, aufbauend auf der Vorlesung TI II: Rechnerarchitektur

Wird mit der Ankündigung bekannt gegeben.

Contents

The proseminar delves more deeply into topics covered in the basic classes taught by the theory group. During the winter semester, we consider advanced topics from the theory of computability and of formal languates (in continuation of "Theory of Computation"); during the summer semester, we talk about algorithms (in continuation of "Algorithms, Data Structures, and Data Abstraction").

Prerequisites

two semesters of computer science, successful completion of "Theory of Computation"

wird mit der Ankündigung bekannt gegeben

In this proseminar we will discuss a selection of articles that have been published in the Software Engineering in Society Track (ICSE-SEIS) within the last years. Topics include diversity, sustainability or privacy, but always with reference to software engineering.

The students choose one of these articles and prepare a presentation (in German or English) in which they present and discuss it besides two relevant and accessible sources. Attention should be paid to the credibility and relevance of the article / study results. Afterwards, the presentation and the corresponding article will be discussed by the seminar participants.

The content of the presentation and the results of the discussion shall be compiled in an paper. In the best case, this paper will be written in parallel to the preparation of the presentation and will then only be revised - taking into account the feedback of the seminar participants. The paper should contain at least 4 pages of pure text (without graphics; the paper with graphics must be correspondingly longer) and will be handed in at the latest 4 weeks after the presentation.

Die zur Auswahl stehenden Artikel finden Sie im Ressourcen-Ordner der KVV-Seite.

Website

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

Interactive Intelligent Systems (IIS) are interactive systems that use artificial intelligence technologies (e.g., from natural language processing). In this seminar, we are concerned with the design, realization, or evaluation of these systems. We combine the complementary perspectives on interactive intelligent systems by considering the used AI technology, user interaction with the system, or both aspects at once. Interactive Intelligent Systems comprise a wide variety of systems or approaches such as AI-infused systems, recommender systems, mixed-initiative user interfaces, or intelligent agents.

Each term, we deal with another particular topic related to the field of Interactive Intelligent Systems field. However, in any case, we focus on a human-centered perspective. We emphasize the interaction between the technical system and the human user in order to explore existing capabilities and limitations in the field of human-computer interaction.

At the beginning of the semester, we provide a general introduction to this year's particular topic. Building on this, we will explore existing approaches, methods, and implementations in the form of student talks, and create a mindmap of the particular topic collaboratively. Each participant is expected to prepare independently, present, and discuss their topic with the class. Based on the results of the discussion, students have to create a written scientific report, which will be evaluated.

Jameson, A., & Riedl, J. (2011). Introduction to the Transactions on Interactive Intelligent Systems. TiiS, 1(1), 1–6. http://doi.org/10.1145/2030365.2030366

Terveen, L. G. (1995). Overview of human-computer collaboration. Knowledge-Based Systems, 8(2-3), 67–81. http://doi.org/10.1016/0950-7051(95)98369-H

Farooq, U., & Grudin, J. (2016). Human-computer integration. Interactions, 23(6), 26–32. http://doi.org/10.1145/3001896

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/Winter-Term-2022_23/methods_and_technologies_in_hci.html

Subject of the project: either development of software together with a company (in this case: 4­ weeks fulltime August/September) or we build a so called NoSQL system. Decision in March. Further information are published in the KVV.

Wird bekannt gegeben. / To be announced.

Target group

Students in the Master's or Bachelor's programme

Prerequisites

Good programming skills, introduction to database systems.

Mixed groups of master and bachelor students will either implement an independent project or are part of a larger project in the area of artifical intelligence and 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 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.

Topic of the software project in WS22/23: Further development of a demonstrator for AI methods to recognize online hate speech.

Coding IxD: Designing Neoanalog Artefacts

In this course, we co-educate computer scientists and product designers. Beyond experiencing interdisciplinary work, we want students to envision interactive systems that are intelligent: by this, we mean an intelligence through code that is carefully using material, form, and context, while profoundly respecting both human capabilities and vulnerabilities.

We understand this course as experimental space, where different perspectives meet, exchange, and evolve. Each semester, based on small project teams of up to five members, students are challenged to examine a specific application context. Within this context, the teams envision a new application or product concept.

We guide this process through various carefully tuned methods that are used to spark their ideas. Students iterate through several rounds of ideation and refine their concept in different prototype versions. The most compelling or promising interaction concept, the one that allows grasping the quality and essence of the product concept is implemented in a working prototype.

Students are accompanied by a team of experienced designers and computer scientists but also by guest experts that provide feedback to the various design iterations. If needed, special workshops are organized to cover specific topics ranging from prototyping to project management. The whole course is evaluated continuously to enhance our methodological toolbox.

This course offering is a cooperation of the r Human-Centered Computing Research Group at the Institute of Computer Science at the Freie Universität Berlin and the Product Design Department at the Weißensee Kunsthochschule Berlin (KHB).

Besides the regular weekly meetings, the KHB provides complimentary workshops each Monday from 10 AM to 1 PM where participation for computer science students is optional.

Zimmerman, John, Jodi Forlizzi, and Shelley Evenson. "Research through design as a method for interaction design research in HCI." Proceedings of the SIGCHI conference on Human factors in computing systems. 2007.

Pierce, James, et al. "Expanding and refining design and criticality in HCI." Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015.

Anthony Dunne and Fiona Raby. 2013. Speculative Everything: Design, Fiction, and Social Dreaming. The MIT Press.

Sophienstrasse 22a, 2.HH, 2.Stock, 10178 Berlin

www.interdisciplinary-laboratory.hu-berlin.de

This course teaches agile methods (Scrum and XP) and open source tools using a single semester-long project. It takes place online and across multiple universities.

Topics covered are:

• Agile methods and related software development processes
• Scrum roles, process practices, including product and engineering management
• Technical practices like refactoring, continuous integration, and test-driven development
• Principles and best practices of open source software development

The project is a software development project in which each student team works with an industry partner who provides the idea for the project. This is a practical hands-on experience. Students take on the role of a software developer. In this role, they estimate and evaluate the effort of requirements and implement them in the project.

Students will be organized into teams of 7-8 people, combining product owners with software developers. An industry partner will provide requirements to be worked out in detail by the product owners and to be realized by the software developers. The available projects will be presented in the run-up to the course.

The course consists of 90min lectures (participation voluntary) followed by a 90min team meeting (participation mandatory).
 

The schedule as well as further information about the course can be viewed via the following Google Spreadsheet: https://amos.uni1.de - here you will also find the registration link for the course.

http://goo.gl/5Wqnr7

Educational objectives and competencies

• Students learn about software products and software development in an industry context
• Students learn about agile methods, in particular Scrum and Extreme Programming
• Students learn about open source software development and its underlying principles
• Students gain practical hands-on experience with a Scrum process and XP technical practices

Target group

Students of computer science (and related fields). If you want to play the software developer role, you should have had practical programming experience. This is not a course to learn programming.

Language

English (lectures in English, team meeting German or English by choice of student team)

Other

• SWS: 4 SWS (2 SWS lecture, 2 SWS team meeting)
• Semester: Every winter semester
• Modality: Online, across multiple universities
• Tags: Scrum

A compiler is a piece of software that translates a computer program from a higher programming language to another programming language (usually machine code). In general, this translation proceeds in several phases, the most important being lexical analysis, syntax analysis, semantic analysis, and code generations. Using lexical and syntactic analysis, we can translate the source program into a representation that is suitable for processing by a computer (abstract syntax tree). This intermediate representation serves as a starting point for optimization and code generation. The methods presented in this class can be applied in many fields of computer science. Therefore, the topics covered in this class are also interesting to students who do intend never to implement a compiler on their own.

Zur Beschaffung empfohlen:

• Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman: Compilers - Principles, Techniques , & Tools, Pearson International Edition, 2007

Die deutsche Version wegen Mängel in der Übersetzung nicht so sehr zu empfehlen:

• Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman: Compiler, Pearson Studium, 2008

Weitere, ergänzende Literatur:

• Helmut Seidl: Compilerbau, TUM, SS08
• Pat D Terry: Compiling with C# and Java, Pearson Education 2005
• Reinhard Wilhelm, Dieter Maurer: Übersetzerbau - Theorie, Konstruktion, Generierung, Springer-Verlag, 2. Auflage 1997
• Niklaus Wirth: Grundlagen und Techniken des Compilerbaus, 3. Auflage, Oldenbourg-Verlag, 2011

This class is part of the "applied computer science" curriculum.

Prerequisites

Basic knowledge of automata theory, formal languages, computer organization, and programming languages

When studying Computer Science at university you mainly focus on concepts. This approach generally makes sense as these conceps are far more persistent and applicable in a broader sense than concrete details would be. Many details, however, which are important for concrete software development, are falling by the wayside. The course is supposed to reduce this deficit.

In it we mainly focus on concepts too, but always only on those which directly have to do with software development, and make sure to apply them precisely, personally in practice -- reflecting its use jointly (something which distinguisdes this course from most other software projects).

The concepts dealt with and practiced may be assigned to three different but closeky connected spheres:

• software development and structuring (object-oriented) Softwareentwurf und -strukturierung (und zwar objektorientiert)
• approaches (for example in the areas prototyping, automatisation, incremental improvement)
• personality development (aspects like consistency, responsibility, communicational skills)

Important: Each participant needs to have a software project of his/her own, which has been started far in advance or the course (within a company, for founding a company or as an open source project), on which he/she works on a weekly basis for the entire duration of the course (mainly in a team) and which serves as training ground for the concepts.

This is a hard prerequisite for participation.

Hauptquelle für die besprochenen Praktiken ist die Website http://clean-code-developer.de

Prerequisites

Essential prerequisite for participation is a software project as described above which is continued for the entire duration of the course with at least 6 hours per week time and effort for the covered topics. 

Target Group

Students who by intensive training wish to establish skills needed by good and professionally working software developers.

Classification & Credits

Wahlveranstaltung for the Master in Computer Science with 5 credits.

Sources

Main source for the concepts dealt with is the website http://clean-code-developer.de

Website

http://www.mi.fu-berlin.de/w/SE/KursProfessionelleSWEntwicklung2022

The official course website can be found here: https://www.wiwiss.fu-berlin.de/fachbereich/bwl/pwo/rothe/teaching/ma_tech-entrepreneurship.html

Secure software engineering joins two important fields: Software engineering and information security. software engineering is the systematic use of principles, methods and tools to develop and deploy software. information security covers topics like confidentiality, integrity and availability of informations and data.

- Claudia Eckert: IT-Sicherheit, 9. Auflage, De Gruyter Oldenbourg, 2014; - Ross Anderson: Security Engineering, 2. Auflage, Wiley, 2008. Weitere Literaturhinweise werden zu den einzelnen Themenblöcken bereitgestellt.

The goal of this lecture is to teach principles, methods and tools for the development of secure software applications. To this end, basic concepts are first introduced. This is followed by process models for developing secure software and evaluating the maturity of development processes. Along the phases or process groups of software development, central principles, methods and tools are then introduced and explained. Special attention is given to threat and risk analysis, security requirements, principles and patterns for designing secure software applications, secure and insecure software implementations, security tests and evaluation of the security properties of software applications.

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

• 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.

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.

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.

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2022_23/course_data_visualization.html

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

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

In dieser VL werden die Grundlagen von KI Systemen mit Datenschutz gelehrt. Dies umfasst insbesondere biometrische KI-basierte biometrische Systeme (z.B. Gesichtserkennung). Es werden Architekturen (VAE + GAN Architekturen) mit impliziertem Datenschutz (vor allem Differential Privacy DP) und verschiedene Implementierungen  vorgestellt. Die mathematischen Grundlagen von KI mit DP werden grundlegend analysiert und mögliche Beweisbarkeits-/ Zertifizierungs-Ansätze ausgeführt.

Ziel der VL ist es ein grundlegendes Verständnis von KI-Systemen/Algorithmen mit Datenschutz.

The official course website can be found here: https://www.wiwiss.fu-berlin.de/fachbereich/bwl/pwo/rothe/teaching/ma_tech-entrepreneurship.html

Secure software engineering joins two important fields: Software engineering and information security. software engineering is the systematic use of principles, methods and tools to develop and deploy software. information security covers topics like confidentiality, integrity and availability of informations and data.

- Claudia Eckert: IT-Sicherheit, 9. Auflage, De Gruyter Oldenbourg, 2014; - Ross Anderson: Security Engineering, 2. Auflage, Wiley, 2008. Weitere Literaturhinweise werden zu den einzelnen Themenblöcken bereitgestellt.

The goal of this lecture is to teach principles, methods and tools for the development of secure software applications. To this end, basic concepts are first introduced. This is followed by process models for developing secure software and evaluating the maturity of development processes. Along the phases or process groups of software development, central principles, methods and tools are then introduced and explained. Special attention is given to threat and risk analysis, security requirements, principles and patterns for designing secure software applications, secure and insecure software implementations, security tests and evaluation of the security properties of software applications.

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

• 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.

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.

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.

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2022_23/course_data_visualization.html

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

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

In dieser VL werden die Grundlagen von KI Systemen mit Datenschutz gelehrt. Dies umfasst insbesondere biometrische KI-basierte biometrische Systeme (z.B. Gesichtserkennung). Es werden Architekturen (VAE + GAN Architekturen) mit impliziertem Datenschutz (vor allem Differential Privacy DP) und verschiedene Implementierungen  vorgestellt. Die mathematischen Grundlagen von KI mit DP werden grundlegend analysiert und mögliche Beweisbarkeits-/ Zertifizierungs-Ansätze ausgeführt.

Ziel der VL ist es ein grundlegendes Verständnis von KI-Systemen/Algorithmen mit Datenschutz.

Secure software engineering joins two important fields: Software engineering and information security. software engineering is the systematic use of principles, methods and tools to develop and deploy software. information security covers topics like confidentiality, integrity and availability of informations and data.

- Claudia Eckert: IT-Sicherheit, 9. Auflage, De Gruyter Oldenbourg, 2014; - Ross Anderson: Security Engineering, 2. Auflage, Wiley, 2008. Weitere Literaturhinweise werden zu den einzelnen Themenblöcken bereitgestellt.

The goal of this lecture is to teach principles, methods and tools for the development of secure software applications. To this end, basic concepts are first introduced. This is followed by process models for developing secure software and evaluating the maturity of development processes. Along the phases or process groups of software development, central principles, methods and tools are then introduced and explained. Special attention is given to threat and risk analysis, security requirements, principles and patterns for designing secure software applications, secure and insecure software implementations, security tests and evaluation of the security properties of software applications.

The course provides an overview of machine learning (ML) methods and algorithms for data science. It consists of two parts:

The first part of the course discusses the emergence of big data, presents common ML-based visualization techniques for large datasets, and introduces basic ML methods such as principal component analysis, k-means clustering, linear regression, and linear discriminant.

The second part of the course introduces advanced ML techniques (probabilistic models, kernels, and deep learning) which address the uncertainty of the model and the nonlinearity of real-world data. It also covers the question of reproducibility and explainability of ML models.

By the end of the course, the student has become familiar with common ML models for data science, has understood their theoretical and algorithmic foundations, and has gained experience on how to use these models adequately in practical situations.

List of topics:

- Big data and data science

- ML-based data visualization

- Principal components, anomalies, and clustering

- Correlation analysis, linear regression, and linear discriminant

- Probabilistic models

- Kernel methods

- Deep learning

- Reproducibility

- Explainable AI

This class focuses on the design and analysis of algorithms, with topics including:

• general principles of algorithm design and analysis,
• 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.

• Cormen, Leiserson, Rivest, Stein: Introduction to Algorithms, 2nd Ed. McGraw-Hill 2001
• Kleinberg, Tardos: Algorithm Design Addison-Wesley 2005.

Target audience

All Master and Bachelor students who are interested in algorithms.

Prerequisites

Basic familiarity with the design and analysis of algorithms.

Cryptoanalysis of asymmetrical schemes

The lecture deals with different asymmetrical cryptanalytics, in particular with the supposed hard problems of these processes. Some of the contents are

• RSA and the problem of factorization
• DSA and the discrete logarithm problem
• Merkel-Hellman and the knapsack and grid problem
• McEliece and the problem of decoding
• Matsumoto-Imai and the multivariate Polynomial System

Knowledge in the areas of IT security and cryptography is obligatory.

Cryptoanalysis of asymmetrical schemes

The lecture deals with different asymmetrical cryptanalytics, in particular with the supposed hard problems of these processes. Some of the contents are

• RSA and the problem of factorization
• DSA and the discrete logarithm problem
• Merkel-Hellman and the knapsack and grid problem
• McEliece and the problem of decoding
• Matsumoto-Imai and the multivariate Polynomial System

Knowledge in the areas of IT security and cryptography is obligatory.

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

• 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.

This course gives an introduction to cryptography and cryptographic key management, as well as an introduction to cryptographic protocols and their application in the field of security in distributed systems. Relevant mathematical tools will be developed accordingly. In addition, the lecture addresses the importance of implementation details in the context of IT system security.

• Jonathan Katz and Yehuda Lindell, Introduction to Modern Cryptography, 2008
• Lindsay N. Childs, A Concrete Introduction to Higher Algebra. Springer Verlag, 1995.
• Johannes Buchmann, Einfuehrung in die Kryptographie. Springer Verlag, 1999.

Weitere noch zu bestimmende Literatur und Primärquellen.

Requirements: Participants must have a good mathematical understanding and good knowledge of computer security and networking.

• propositional logic and mathematical proof techniques
• Boolean formulae und Boolean functions, disjunctive and conjunctive normal form, satisfiability, resolution calculus
• set theory:  sets, relations, equivalence and order relations, functions
• natural numbers, the induction principle, cardinal numbers
• first-order logic und mathematical structures
• combinatorics: counting techniques, binomial coefficents and Stirling numbers, recurrences, pigeon hole principle
• graph theory: graphs and their representations, paths and cycles in graphs, trees

• Christoph Meinel, Martin Mundhenk: Mathematische Grundlagen der Informatik, Teubner; 2. Auflage 2002
• Uwe Schöning: Logik für Informatiker, B.I.-Wissenschaftsverlag; 5. Auflage 2000
• Kenneth H. Rosen: Discrete Mathematics and its Applications, Mc-Graw Hill; 1999
• M. Aigner: Diskrete Mathematk, Vieweg, 5. Auflage 2004

Content:
This is the first part of a three semester introduction into the basic mathematical field of Analysis. Differential and integral calculus in a real variable will be covered. Topics:

1. fundamentals, elementary logic, ordered pairs, relations, functions, domain and range of a function, inverse functions (injectivity, surjectivity)
2. numbers, induction, calculations in R, C
3. arrangement of R, maximum and minimum, supremum and infimum of real sets, supremum / infimum completeness of R, absolute value of a real number, Q is dense in R
4. sequences and series, limits, cauchy sequences, convergence criteria, series and basic principles of convergence
5. topological aspects of R, open, closed, and compact real sets
6. sequences of functions, series of functions, power series
7. properties of functions, boundedness, monotony, convexity
8. continuity, limits and continuity of functions, uniform continuity, intermediate value theorems, continuity and compactness
9. differentiability, concept of the derivative, differentiation rules, mean value theorem, local and global extrema, curvature, monotony, convexity
10. elementary functions, rational functions, root functions, exponential functions, angular functions, hyperbolic functions, real logarithm, inverse trigonometric functions, curve sketching
11. beginnings of integral calculus

Detailed Information can be found on the Homepage of 19202801 Analysis I.

Literature:

• Bröcker, Theodor: Analysis 1, Spektrum der Wissenschaft-Verlag.
• Forster, Otto: Analysis 1, Vieweg-Verlag.
• Spivak, Michael: Calculus, 4th Edition.

Viele Analysis Bücher sind auch über die Fachbibliothek der FU Berlin elektronisch verfügbar.

Bei Schwierigkeiten mit den Grundbegriffen Menge, Abbildung etc. ist die folgende Ausarbeitung empfehlenswert:

• Scheerer, Hans: Brückenkurs, Skript FU Berlin 2006.

Content:

• Basic terms/concepts: sets, maps, equivalence relations, groups, rings,
• fields
• Linear equation systems: solvability criteria, Gauss algorithm
• Vector spaces: linear independence, generating systems and bases, dimension,
• subspaces, quotient spaces, cross products in R3
• Linear maps: image and rank, relationship to matrices, behaviour under
• change of basis
• Dual vector spaces: multilinear forms, alternating and symmetric bilinear
• forms, relationship to matices, change of basis
• Determinants: Cramer's rule, Eigenvalues and Eigenvectors

Prerequisites:

Participation in the preparatory course (Brückenkurs) is highly recommended.

• Siegfried Bosch, Lineare Algebra, 4. Auflage, Springer-Verlag, 2008;
• Gerd Fischer, Lernbuch Lineare Algebra und Analytische Geometrie, Springer-Verlag, 2017;
• Bartel Leendert van der Waerden, Algebra Volume I, 9th Edition, Springer 1993;

Zu den Grundlagen

• Kevin Houston, Wie man mathematisch denkt: Eine Einführung in die mathematische Arbeitstechnik für Studienanfänger, Spektrum Akademischer Verlag, 2012

Contents:

• number systems: from natural numbers to real numbers, completeness property of the reals
• polynomials: roots of polynomials, polynomial interpolation, rational functions
• special functions:  exponential function, logarithm, trigonometric functions
• complex numbers: exponential function for complex numbers, complex roots
• convergence of sequences and series, convergence of functions, continuous functions, O-notation
• differential calculus: derivative of a function,  interpretations and applications of the derivative
• intergral calculus: primitive functions, definite integrals,  fundamental theorem of calculus, applications
• power series
• basics of stochastics: probability spaces, discrete and continuous random variables, expected value and variance of random variables

• Kurt Meyberg, Peter Vachenauer: Höhere Mathematik 1, Springer-Verlag, 6. Auflage 2001
• Dirk Hachenberger: Mathematik für Informatiker, Pearson 2005
• Peter Hartmann: Mathematik für Informatiker, Vieweg, 4. Auflage 2006
• Thomas Westermann: Mathematik für Ingenieure mit Maple 1, Springer-Verlag, 4. Auflage 2005

The sign-up for the tutorial sessions will be announced in due time.

Content:
This is the first part of a three semester introduction into the basic mathematical field of Analysis. Differential and integral calculus in a real variable will be covered. Topics:

1. fundamentals, elementary logic, ordered pairs, relations, functions, domain and range of a function, inverse functions (injectivity, surjectivity)
2. numbers, induction, calculations in R, C
3. arrangement of R, maximum and minimum, supremum and infimum of real sets, supremum / infimum completeness of R, absolute value of a real number, Q is dense in R
4. sequences and series, limits, cauchy sequences, convergence criteria, series and basic principles of convergence
5. topological aspects of R, open, closed, and compact real sets
6. sequences of functions, series of functions, power series
7. properties of functions, boundedness, monotony, convexity
8. continuity, limits and continuity of functions, uniform continuity, intermediate value theorems, continuity and compactness
9. differentiability, concept of the derivative, differentiation rules, mean value theorem, local and global extrema, curvature, monotony, convexity
10. elementary functions, rational functions, root functions, exponential functions, angular functions, hyperbolic functions, real logarithm, inverse trigonometric functions, curve sketching
11. beginnings of integral calculus

Detailed Information can be found on the Homepage of 19202801 Analysis I.

Literature:

• Bröcker, Theodor: Analysis 1, Spektrum der Wissenschaft-Verlag.
• Forster, Otto: Analysis 1, Vieweg-Verlag.
• Spivak, Michael: Calculus, 4th Edition.

Viele Analysis Bücher sind auch über die Fachbibliothek der FU Berlin elektronisch verfügbar.

Bei Schwierigkeiten mit den Grundbegriffen Menge, Abbildung etc. ist die folgende Ausarbeitung empfehlenswert:

• Scheerer, Hans: Brückenkurs, Skript FU Berlin 2006.

Content:

• Basic terms/concepts: sets, maps, equivalence relations, groups, rings,
• fields
• Linear equation systems: solvability criteria, Gauss algorithm
• Vector spaces: linear independence, generating systems and bases, dimension,
• subspaces, quotient spaces, cross products in R3
• Linear maps: image and rank, relationship to matrices, behaviour under
• change of basis
• Dual vector spaces: multilinear forms, alternating and symmetric bilinear
• forms, relationship to matices, change of basis
• Determinants: Cramer's rule, Eigenvalues and Eigenvectors

Prerequisites:

Participation in the preparatory course (Brückenkurs) is highly recommended.

• Siegfried Bosch, Lineare Algebra, 4. Auflage, Springer-Verlag, 2008;
• Gerd Fischer, Lernbuch Lineare Algebra und Analytische Geometrie, Springer-Verlag, 2017;
• Bartel Leendert van der Waerden, Algebra Volume I, 9th Edition, Springer 1993;

Zu den Grundlagen

• Kevin Houston, Wie man mathematisch denkt: Eine Einführung in die mathematische Arbeitstechnik für Studienanfänger, Spektrum Akademischer Verlag, 2012

In the course of the seminar, students deal with topics from the fields of data visualization and data mining.

Wird bekannt gegeben.

Preliminary discussion date to be announced.

Worst-case analysis of algorithms is one of the cornerstones of theoretical computer science. At the same time, there is a growing discrepancy between the theoretical worst-case guarantees we can prove and the practical performance of algorithms observed on real inputs. This is particularly true in fields such as machine learning, SAT solving, and numerical optimization, where problems known to be hard in the worst-case are routinely solved to great effect on large real-world inputs.

In this seminar, we look at different strategies for dealing with this situation, and more fine-grained and nuanced techniques for the analysis of algorithms. The discussions will be mostly based on chapters of the recent book: "Beyond the Worst-Case Analysis of Algorithms" by T. Roughgarden, Cambridge University Press, 2020.

Requirement: ALP3/HA or similar algorithmic background, mathematical maturity.

In the course of the seminar, students deal with topics from the fields of data visualization and data mining.

Wird bekannt gegeben.

Preliminary discussion date to be announced.

Description:

This seminar covers different topics from the research area of autonomous cars. We will focus on questions which can be the basis for Bachelor or Master theses as:

vision based person or vehicle recognition, map generation from trajectories, V2V and V2X communication, object tracking and prediction, sensory data fusion, localization using map matching, recognition of drivable areas and free spaces using stereo vision, path planning with A*, RRT, potential fields, automatic sensor calibration, map data formats.

Students can propose interesting topics, too. The seminar will be taught as a block course.

Subject of the project: either development of software together with a company (in this case: 4­ weeks fulltime August/September) or we build a so called NoSQL system. Decision in March. Further information are published in the KVV.

Wird bekannt gegeben. / To be announced.

Target group

Students in the Master's or Bachelor's programme

Prerequisites

Good programming skills, introduction to database systems.

Coding IxD: Designing Neoanalog Artefacts

In this course, we co-educate computer scientists and product designers. Beyond experiencing interdisciplinary work, we want students to envision interactive systems that are intelligent: by this, we mean an intelligence through code that is carefully using material, form, and context, while profoundly respecting both human capabilities and vulnerabilities.

We understand this course as experimental space, where different perspectives meet, exchange, and evolve. Each semester, based on small project teams of up to five members, students are challenged to examine a specific application context. Within this context, the teams envision a new application or product concept.

We guide this process through various carefully tuned methods that are used to spark their ideas. Students iterate through several rounds of ideation and refine their concept in different prototype versions. The most compelling or promising interaction concept, the one that allows grasping the quality and essence of the product concept is implemented in a working prototype.

Students are accompanied by a team of experienced designers and computer scientists but also by guest experts that provide feedback to the various design iterations. If needed, special workshops are organized to cover specific topics ranging from prototyping to project management. The whole course is evaluated continuously to enhance our methodological toolbox.

This course offering is a cooperation of the r Human-Centered Computing Research Group at the Institute of Computer Science at the Freie Universität Berlin and the Product Design Department at the Weißensee Kunsthochschule Berlin (KHB).

Besides the regular weekly meetings, the KHB provides complimentary workshops each Monday from 10 AM to 1 PM where participation for computer science students is optional.

Zimmerman, John, Jodi Forlizzi, and Shelley Evenson. "Research through design as a method for interaction design research in HCI." Proceedings of the SIGCHI conference on Human factors in computing systems. 2007.

Pierce, James, et al. "Expanding and refining design and criticality in HCI." Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015.

Anthony Dunne and Fiona Raby. 2013. Speculative Everything: Design, Fiction, and Social Dreaming. The MIT Press.

Sophienstrasse 22a, 2.HH, 2.Stock, 10178 Berlin

www.interdisciplinary-laboratory.hu-berlin.de

When studying Computer Science at university you mainly focus on concepts. This approach generally makes sense as these conceps are far more persistent and applicable in a broader sense than concrete details would be. Many details, however, which are important for concrete software development, are falling by the wayside. The course is supposed to reduce this deficit.

In it we mainly focus on concepts too, but always only on those which directly have to do with software development, and make sure to apply them precisely, personally in practice -- reflecting its use jointly (something which distinguisdes this course from most other software projects).

The concepts dealt with and practiced may be assigned to three different but closeky connected spheres:

• software development and structuring (object-oriented) Softwareentwurf und -strukturierung (und zwar objektorientiert)
• approaches (for example in the areas prototyping, automatisation, incremental improvement)
• personality development (aspects like consistency, responsibility, communicational skills)

Important: Each participant needs to have a software project of his/her own, which has been started far in advance or the course (within a company, for founding a company or as an open source project), on which he/she works on a weekly basis for the entire duration of the course (mainly in a team) and which serves as training ground for the concepts.

This is a hard prerequisite for participation.

Hauptquelle für die besprochenen Praktiken ist die Website http://clean-code-developer.de

Prerequisites

Essential prerequisite for participation is a software project as described above which is continued for the entire duration of the course with at least 6 hours per week time and effort for the covered topics. 

Target Group

Students who by intensive training wish to establish skills needed by good and professionally working software developers.

Classification & Credits

Wahlveranstaltung for the Master in Computer Science with 5 credits.

Sources

Main source for the concepts dealt with is the website http://clean-code-developer.de

Website

http://www.mi.fu-berlin.de/w/SE/KursProfessionelleSWEntwicklung2022

Subject of the project: either development of software together with a company (in this case: 4­ weeks fulltime August/September) or we build a so called NoSQL system. Decision in March. Further information are published in the KVV.

Wird bekannt gegeben. / To be announced.

Target group

Students in the Master's or Bachelor's programme

Prerequisites

Good programming skills, introduction to database systems.

Coding IxD: Designing Neoanalog Artefacts

In this course, we co-educate computer scientists and product designers. Beyond experiencing interdisciplinary work, we want students to envision interactive systems that are intelligent: by this, we mean an intelligence through code that is carefully using material, form, and context, while profoundly respecting both human capabilities and vulnerabilities.

We understand this course as experimental space, where different perspectives meet, exchange, and evolve. Each semester, based on small project teams of up to five members, students are challenged to examine a specific application context. Within this context, the teams envision a new application or product concept.

We guide this process through various carefully tuned methods that are used to spark their ideas. Students iterate through several rounds of ideation and refine their concept in different prototype versions. The most compelling or promising interaction concept, the one that allows grasping the quality and essence of the product concept is implemented in a working prototype.

Students are accompanied by a team of experienced designers and computer scientists but also by guest experts that provide feedback to the various design iterations. If needed, special workshops are organized to cover specific topics ranging from prototyping to project management. The whole course is evaluated continuously to enhance our methodological toolbox.

This course offering is a cooperation of the r Human-Centered Computing Research Group at the Institute of Computer Science at the Freie Universität Berlin and the Product Design Department at the Weißensee Kunsthochschule Berlin (KHB).

Besides the regular weekly meetings, the KHB provides complimentary workshops each Monday from 10 AM to 1 PM where participation for computer science students is optional.

Zimmerman, John, Jodi Forlizzi, and Shelley Evenson. "Research through design as a method for interaction design research in HCI." Proceedings of the SIGCHI conference on Human factors in computing systems. 2007.

Pierce, James, et al. "Expanding and refining design and criticality in HCI." Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015.

Anthony Dunne and Fiona Raby. 2013. Speculative Everything: Design, Fiction, and Social Dreaming. The MIT Press.

Sophienstrasse 22a, 2.HH, 2.Stock, 10178 Berlin

www.interdisciplinary-laboratory.hu-berlin.de