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 Fundamentals of Computer Engineering forms the basis for understanding the functioning of real computer systems.

Starting from logic, this module primarily covers the subject areas of switching networks and switching mechanisms, logic minimization, gates, flip-flops, memory, automatic machines and simple hardware design.

Furthermore, basic knowledge of DC and AC networks, semiconductors, transistors, CMOS, operational amplifiers, A/D and D/A converters is taught, as far as it is necessary for computer science.

In the corresponding practical course, the knowledge gained in the lecture and in the module Computer Architecture, Operating and Communication Systems are deepened with numerous practical experiments.

Based on a simple hardware platform with processor and various interfaces, the participants will learn how to program elementary drivers, extend operating system routines and control the interfaces.

Subsequently, the systems should be networked and be able to interact with their environment.

Siehe Unterlagen auf der Webseite - nur aus dem FB-Netz oder über VPN Webseite
 

See documentation on the website - only from the FB network or via VPN Webseite

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

The overwhelming majority of future computer systems will be characterized by embedded systems that communicate with each other. These can be found in machine controls, household appliances, motor vehicles, airplanes, intelligent buildings, etc., and will be increasingly integrated into networks such as the Internet in the future.

This practical project will deal with the architecture of embedded systems and will show the differences to traditional PC architectures (e.g. real-time capability, interaction with the environment) using practical examples. It is based on 16- or 32-bit microcontroller systems.

The focus of the practical project, which is divided into individual experiments, is

• Register structures
• Storage organization
• hardware-oriented assembler and high-level language programming
• I/O and timer programming
• Interrupt System
• Watchdog Logic
• Analogue interfaces
• Bus system connection of components
• Communication (serial, CAN bus, Ethernet, radio and USB)
• Control of models and use of different sensor technology

• Brian W. Kernighan, Dennis M. Ritchie: The C Programming Language, Second Edition, Prentice Hall, 1988.

See documents on the website - only from the FB network or via VPN website

Group A, Wednesday, 12-16 h Takustraße 9, Room K63
Group B, Thursday, 12-16 h Takustraße 9, Room K63

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

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

Proseminar: The power of computer science regarding safety & security issues

There are numerous cases where safety and security solutions make use of information technology, e.g. in the case of surveillance systems, sensor networks, software applications and many more.

By the means of selected information systems that are currently being developed for real-world deployment, the impact of information science will be presented and discussed. Possible examples could be PreCobs (Forecasting software for criminality), specific video  tracking software and other approaches.

Students get the chance to either choose examples for information systems themselves or select from given thematic areas. Additionally during the seminar, specific solutions for surveillance, warning systems, (food, medical etc.) supply will be observed and evaluated in an external demonstration room students will have exclusive access to.

In order to develop those thematic areas fundamental skills of academic work are to be conveyed.

Literatur (englisch und deutsch) wird zum Beginn der Veranstaltung bekannt gegeben.

Technological impact assessment intends to make transparent how technology develops in social surroundings and which chances and risks are connected with its use.

Technological impact assessment

• analyses the impact of technology on society, environment, health, politics, economy
• focuses on potential intended as well as non-intended consequences
• tries to create options of acting and shaping future development

Within the lecture students will conduct an exemplary technological impact assessment process themselves.

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 open up existing approaches, methods, and implementations in the form of student talks ad 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 gets evaluated.

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2020_21/proseminar_interactive_intelligent_systems.html

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.

je nach Anwendungsgebiet

Within the scope of the software project several projects with medical or veterinary reference are being worked on.

Students develop software in teams, independently as well as under supervision, for and with the involvement of the project customers.

The software project takes place as a block event.

Will be announced.

Target group

Students in the Master's or Bachelor's programme

Prerequisites

• ALP 1
• ALP 2
• ALP 3
• Database Systems

This software project focuses on several aspects of technical Computer Science. At the start of the software project you will receive a list of suggested topics that mainly deal with particular aspects of the so-called Trusted Computing and security issues in the Internet of Things. You are also very welcome to suggest your own topic that is closely related to technical Computer Science. You will work on your topic in a small group of 2-3 students. The collaboration and versioning of the developed code is done via the faculty's own Gitlab or a public Github. The results are to be documented appropriately, e.g. via the readme files of the git and a well structured wiki.

It is possible to combine this software project with the seminar Telematics. Then, the theoretical foundations of the topic are dealt with in the scientific seminar paper and implemented in practice in the software project. Please note that the seminar paper is not supposed to deal with details of the implementation and that you are still obliged to write an accurate documentation of the software project in written form. 

Concerning the schedule: This software project takes place during the semester. There are only a few meetings with all participants, but these are mandatory. In addition, there are short weekly meetings in which at least 1 group member reports on the current status, whereby we would like to have spoken to all members in the course of a month. On the first meeting (03.11.2020), the topic list will be handed out and discussed. Please prepare a short (2-3 minutes) overview of your own topic suggestion if you would like to include it in the software project. On the next week (10.11.2020), the topics will be assigned. After that there will be 3 presentation dates in total: the topic presentation (08.12.2021), a short interim presentation (19.01.2021) and the final presentation (02.03.2021). This semester, all meetings will take place as video conferences with Webex.

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.

Corporate Semantic Web

Corporate Semantic Web

Further information can be found on the course website

In this software project we will carry out the entire process of designing, implementing, and evaluating mobile devices (mainly Android) with a focus on usable security. We will work in small groups.

Tasks:

1. Further development of existing authentication methods and adaptation to smartphones.

2. Development of new authentication methods (some ideas already exist but new ones can also be incorporated).

3. E- mail encryption on mobile devices.

Requirements:

1. Knowledge of programming on Android devices (Alternatively, for iOS). There will be a brief preliminary test.

2. Interest in Security and Usability.

3. Participation in the planning meeting (end of summer term 2016).

Programming for Internet Communication Along the Stack

In this course, students will develop solutions that relate to Internet communication. We will consider all aspects of the Internet ecosystem, including lower layers up to the application layer for the current Internet as well as the Internet of Things. Participants will work in smaller groups (3-4 students), where each group will focus on a specific builing block. This will allow to adapt to interests and expertises (e.g., web vs. embedded system programming).

Note that we intend to use the results of this software project to revamp the old super computers in the entrance area of the CS building (i.e., the PDP 11 and the Russian clone). We will extend both computers by network-capabale devices, sensors, screens, and your software (a) to demonstrate the Internet of Things and (b) to give insights into the current state of the Internet backbone.

Below you find some initial topics, which we will clarify during the first meeting.

Embedded / IoT Programming

You will develop applications for embedded devices. The software base is the open source operating system RIOT.

Internet Backbone Measurements

You will develop applications that process live BGP data and check if an attack is happening. The software base is the RTRlib and BGPMON/BGPStream.

Web Interface

You will develop a web interfaces that shows the state of the super computers to remote participants. Your web frontend should be suitable for mobile devices.

If you have questions in advance, feel free to contact Matthias Wählisch.

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/KursProfessionelleSWEntwicklung2020

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 rapid technological development requires the processing of large amounts of data of various kinds in order to make them usable by humans. This challenge affects many areas of life today. Especially in research, economy and politics data visualization is used to explain information and correlations by graphical representation, to explore them by visual analysis and to support decision making. The goal of this course is to familiarize students with the principles, techniques and algorithms of data visualization and to develop practical skills for designing and implementing data visualizations.

This course teaches students the fundamentals of data visualization with current content from research and practice. At the end of the course the students will:

1. know essential theoretical basics of visualization for graphical perception and cognition,

2. know and be able to apply methods for the visual coding of data, as well as methods of interactive visualisation

3. understand and be able to apply algorithms and techniques for visualising data (diagrams, graphs, maps), including methods of interaction

4. be able to critically evaluate visualization solutions, and

5. have practical skills in the design of visualisations and their implementation.

The course is aimed at students who are interested in using data visualization in their work as well as students who want to develop visualization tools. Basic knowledge in programming (Javascript, Python) and data analysis (e.g. R) is helpful.

Besides participating in the discussions in the course, students complete several programming and data analysis tasks, as well as a final project as an executable visualization tool. Students are expected to document and present the results of the tasks and the project.

Please note that the course focuses on how data is visually coded, presented and analyzed once the structure of the data and its content is known. Explorative analytical methods for discovering insights in data are not the focus of the course.

Text Book

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

Additional Literature

• Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. 2017. 

• 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_2020_21/course_data_visualization.html

Goal:

After this course the students

• have a deep understanding of design principles of security protocols and security infrastructures.
• have knowledge of the basic security goals in cryptography and its relevance to practical use cases.
• understand, in which way well-known security protocols (TLS, PACE, EAC) achieve the security goals.
• understand the key topics of the wide-spread security infrastructure standards and apply them to practical tasks.
• are able to choose suitable protocols for a given use case.
• can evaluate the security properties of security protocols and infrastructures.

Content:   

• Security goals (CIA)
• Network security protocols (TLS)
• Security protocols for electronic ID cards
• Abstract Syntax Notation 1 (ASN.1)
• Certificates and related standards X.509/RFC5280
• Public Key Cryptography Standard Series
• Certificate-based security infrastructures (PKI)
• Practical and theoretical solutions to exercises

The lecture Internet of Things (IoT) Security deals with the following topics:

• Current security problems in IoT,
• IT security standards and guidelines for IoT devices,
• Demonstration of security vulnerabilities,
• Tools and procedures for secure development and penetration testing.

In the exercises, security problems are examined using examples and possible solutions are discussed.

The students should learn why current IoT devices have security holes and how to identify them. In addition, solutions and standards are presented, how to develop secure devices, and own solutions are developed and discussed.

In recent years, data science has developed rapidly, primarily due to the progress in machine learning. This development has opened up new opportunities in a variety of social, scientific, and technological areas. From the experience of recent years, however, it is becoming increasingly clear that the concentration on purely statistical and numerical aspects in data science fails to capture social nuances or take ethical criteria into account. The research area Human-Centered Data Science closes this gap at the intersection of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Human Computation, and the statistical and numerical techniques of Data Science.

Human-Centered Data Science (HCDS) focuses on fundamental principles of data science and its human implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness, and accountability; data provenance, curation, preservation, and reproducibility; user experience design and research for big data; human computation; effective oral, written, and visual scientific communication; and societal impacts of data science.

At the end of this course, students will understand the main concepts, theories, practices, and different perspectives on which data can be collected and manipulated. Furthermore, students will be able to realize the impact of current technological developments may have on society.

This course curriculum was initially developed by Jonathan T. Morgan, Cecilia Aragon, Os Keyes, and Brock Craft. We have adapted the curriculum for the European context and our specific understanding of the field

Aragon, C. M., Hutto, C., Echenique, A., Fiore-Gartland, B., Huang, Y., Kim, J., et al. (2016). Developing a Research Agenda for Human-Centered Data Science. (pp. 529–535). Presented at the CSCW Companion, New York, New York, USA: ACM Press. http://doi.org/10.1145/2818052.2855518

Baumer, E. P. (2017). Toward human-centered algorithm design:. Big Data & Society, 4(2), 205395171771885. http://doi.org/10.1177/2053951717718854

Kogan, M., Halfaker, A., Guha, S., Aragon, C., Muller, M., & Geiger, S. (2020, January). Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. In Companion of the 2020 ACM International Conference on Supporting Group Work (pp. 151-156).

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2020_21/course_human_centered_data_science.html

This course gives an introduction to human factors in privacy and security. A variety of privacy and security related issues in user interfaces are discussed. During the course, the students get theoretical as well as practical experience on methods and tools to identify issues in usability, security and privacy in various technologies.

[1] D. Jurafsky, J. Martin: Speech and Language Processing.
see https://web.stanford.edu/~jurafsky/slp3/
[2] The Natural language Toolkit.
see http://www.nltk.org/
[3] J. Allen: Natural Language Understanding (Pearson)
[4] C. Manning, H. Schütze: Foundations of Statistical Natural Language Processing (MIT Press)
[5] S. Russel, P. Norvig: Artificial Intelligence, A Modern Approach (Pearson)
[6] S. Bird, E. Klein, E. Loper: Natural Language Processing with Python (OReilly).

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 rapid technological development requires the processing of large amounts of data of various kinds in order to make them usable by humans. This challenge affects many areas of life today. Especially in research, economy and politics data visualization is used to explain information and correlations by graphical representation, to explore them by visual analysis and to support decision making. The goal of this course is to familiarize students with the principles, techniques and algorithms of data visualization and to develop practical skills for designing and implementing data visualizations.

This course teaches students the fundamentals of data visualization with current content from research and practice. At the end of the course the students will:

1. know essential theoretical basics of visualization for graphical perception and cognition,

2. know and be able to apply methods for the visual coding of data, as well as methods of interactive visualisation

3. understand and be able to apply algorithms and techniques for visualising data (diagrams, graphs, maps), including methods of interaction

4. be able to critically evaluate visualization solutions, and

5. have practical skills in the design of visualisations and their implementation.

The course is aimed at students who are interested in using data visualization in their work as well as students who want to develop visualization tools. Basic knowledge in programming (Javascript, Python) and data analysis (e.g. R) is helpful.

Besides participating in the discussions in the course, students complete several programming and data analysis tasks, as well as a final project as an executable visualization tool. Students are expected to document and present the results of the tasks and the project.

Please note that the course focuses on how data is visually coded, presented and analyzed once the structure of the data and its content is known. Explorative analytical methods for discovering insights in data are not the focus of the course.

Text Book

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

Additional Literature

• Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. 2017. 

• 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_2020_21/course_data_visualization.html

Goal:

After this course the students

• have a deep understanding of design principles of security protocols and security infrastructures.
• have knowledge of the basic security goals in cryptography and its relevance to practical use cases.
• understand, in which way well-known security protocols (TLS, PACE, EAC) achieve the security goals.
• understand the key topics of the wide-spread security infrastructure standards and apply them to practical tasks.
• are able to choose suitable protocols for a given use case.
• can evaluate the security properties of security protocols and infrastructures.

Content:   

• Security goals (CIA)
• Network security protocols (TLS)
• Security protocols for electronic ID cards
• Abstract Syntax Notation 1 (ASN.1)
• Certificates and related standards X.509/RFC5280
• Public Key Cryptography Standard Series
• Certificate-based security infrastructures (PKI)
• Practical and theoretical solutions to exercises

The lecture Internet of Things (IoT) Security deals with the following topics:

• Current security problems in IoT,
• IT security standards and guidelines for IoT devices,
• Demonstration of security vulnerabilities,
• Tools and procedures for secure development and penetration testing.

In the exercises, security problems are examined using examples and possible solutions are discussed.

The students should learn why current IoT devices have security holes and how to identify them. In addition, solutions and standards are presented, how to develop secure devices, and own solutions are developed and discussed.

In recent years, data science has developed rapidly, primarily due to the progress in machine learning. This development has opened up new opportunities in a variety of social, scientific, and technological areas. From the experience of recent years, however, it is becoming increasingly clear that the concentration on purely statistical and numerical aspects in data science fails to capture social nuances or take ethical criteria into account. The research area Human-Centered Data Science closes this gap at the intersection of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Human Computation, and the statistical and numerical techniques of Data Science.

Human-Centered Data Science (HCDS) focuses on fundamental principles of data science and its human implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness, and accountability; data provenance, curation, preservation, and reproducibility; user experience design and research for big data; human computation; effective oral, written, and visual scientific communication; and societal impacts of data science.

At the end of this course, students will understand the main concepts, theories, practices, and different perspectives on which data can be collected and manipulated. Furthermore, students will be able to realize the impact of current technological developments may have on society.

This course curriculum was initially developed by Jonathan T. Morgan, Cecilia Aragon, Os Keyes, and Brock Craft. We have adapted the curriculum for the European context and our specific understanding of the field

Aragon, C. M., Hutto, C., Echenique, A., Fiore-Gartland, B., Huang, Y., Kim, J., et al. (2016). Developing a Research Agenda for Human-Centered Data Science. (pp. 529–535). Presented at the CSCW Companion, New York, New York, USA: ACM Press. http://doi.org/10.1145/2818052.2855518

Baumer, E. P. (2017). Toward human-centered algorithm design:. Big Data & Society, 4(2), 205395171771885. http://doi.org/10.1177/2053951717718854

Kogan, M., Halfaker, A., Guha, S., Aragon, C., Muller, M., & Geiger, S. (2020, January). Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. In Companion of the 2020 ACM International Conference on Supporting Group Work (pp. 151-156).

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2020_21/course_human_centered_data_science.html

This course gives an introduction to human factors in privacy and security. A variety of privacy and security related issues in user interfaces are discussed. During the course, the students get theoretical as well as practical experience on methods and tools to identify issues in usability, security and privacy in various technologies.

[1] D. Jurafsky, J. Martin: Speech and Language Processing.
see https://web.stanford.edu/~jurafsky/slp3/
[2] The Natural language Toolkit.
see http://www.nltk.org/
[3] J. Allen: Natural Language Understanding (Pearson)
[4] C. Manning, H. Schütze: Foundations of Statistical Natural Language Processing (MIT Press)
[5] S. Russel, P. Norvig: Artificial Intelligence, A Modern Approach (Pearson)
[6] S. Bird, E. Klein, E. Loper: Natural Language Processing with Python (OReilly).

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 class focuses on topics such as

• general principles of algorithm design,
• network flows,
• amortised analysis and advanced data structures,
• string matching,
• NP-completeness,
• approximation algorithms for hard problems,
• arithmetic algorithms and circuits, fast fourier transform.

• 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.

In this lecture we look at the effect of quantum computers on cryptography. For that purpose we feed into the function of quantum computers first and then we analyse different cryptographic algorithms. Highlights are the presentation of the quantum algorithms of Grover (to improve cryptoanalysis of symmetrical schemes) an Shor (to improve Cryptoanalysis of schemes based on the factorisation problem and the discrete logarithm problem).

Course objectives 

Security is a prerequisite of modern wireless communication systems and user services. In this course we will introduce a new security paradigm for communication systems called physical layer security (PLS). PLS is an advanced method envisioned to meet the advanced requirements of future communication systems such as low latency ('fast security') and 'plug & secure' functionality. PLS is based on a well-defined mathematical theory exploiting the inherent properties of the wireless channel such as noise and fading. The goal of the course is to present the information-theoretic foundations of PLS including the definition of secrecy capacity definitions for the point-to-point wireless channel with an eavesdropper ('wiretap channel') as well as secret key agreement from stochastic channel measurements.

The course will also take on the attackers view and address potential attacks. Finally, several potential deployment scenarios such as the Internet of Things will be discussed.

Course Content

• Overview of security schemes in communication systems: layer model, asymmetric/symmetric encryption, PLS classifications
• Wiretap channel: basic definitions and assumptions, secrecy capacity, strong/weak secrecy, coding methods, MIMO channel, multiuser aspects
• Secret key agreement: source models, key generation, common randomness, reciprocal wireless channel, correlation properties
• Attacker models: passive/active, 'man in the middle', jamming
• Deployment scenarios: lightweight security, wormhole detection

Matthieu Bloch and Joao Barros: Physical Layer Security: From Information Theory to Security Engineering. Cambridge University Press, 2011

Course objectives 

Security is a prerequisite of modern wireless communication systems and user services. In this course we will introduce a new security paradigm for communication systems called physical layer security (PLS). PLS is an advanced method envisioned to meet the advanced requirements of future communication systems such as low latency ('fast security') and 'plug & secure' functionality. PLS is based on a well-defined mathematical theory exploiting the inherent properties of the wireless channel such as noise and fading. The goal of the course is to present the information-theoretic foundations of PLS including the definition of secrecy capacity definitions for the point-to-point wireless channel with an eavesdropper ('wiretap channel') as well as secret key agreement from stochastic channel measurements.

The course will also take on the attackers view and address potential attacks. Finally, several potential deployment scenarios such as the Internet of Things will be discussed.

Course Content

• Overview of security schemes in communication systems: layer model, asymmetric/symmetric encryption, PLS classifications
• Wiretap channel: basic definitions and assumptions, secrecy capacity, strong/weak secrecy, coding methods, MIMO channel, multiuser aspects
• Secret key agreement: source models, key generation, common randomness, reciprocal wireless channel, correlation properties
• Attacker models: passive/active, 'man in the middle', jamming
• Deployment scenarios: lightweight security, wormhole detection

Matthieu Bloch and Joao Barros: Physical Layer Security: From Information Theory to Security Engineering. Cambridge University Press, 2011

In this lecture we look at the effect of quantum computers on cryptography. For that purpose we feed into the function of quantum computers first and then we analyse different cryptographic algorithms. Highlights are the presentation of the quantum algorithms of Grover (to improve cryptoanalysis of symmetrical schemes) an Shor (to improve Cryptoanalysis of schemes based on the factorisation problem and the discrete logarithm problem).

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

Contents: - the real numbers, - the concept of limits (sequences and series) – functions (continuity and differentiability) – minima and maxima of differentiable functions, extreme value calculation – the Riemann integral

Die Vorlesung folgt dem Skript Analysis I

Weitere Literatur:

• Blatter, Ch.: Analysis 1. Springer-Verlag. xi+369 S.

• Forster, O.: Analysis 1. Differential- und Integralrechnung einer Veränderlichen. Vieweg. viii+208 S.

• Fritzsche, K.: Grundkurs Analysis 1 - Differentiation und Integration in einer Veränderlichen. Spektrum Verlag. xii+372 S.

Zahlreiche Beispiele, Anwendungen und nützliche Anmerkungen findet man in

• Arens, T. , et al.: Mathematik. Heidelberg: Spektrum Akademischer Verlag. 2008. xiv+1496 S.

Das folgende Buch entwickelt die Analysis aus dem historischen Kontext

• Körle, H.-H.: Die phantastische Geschichte der Analysis. Ihre Probleme und Methoden seit Demokrit und Archimedes. Dazu die Grundbegriffe von heute. Oldenbourg. 2009. xiv+217 S.

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.

Contents: - the real numbers, - the concept of limits (sequences and series) – functions (continuity and differentiability) – minima and maxima of differentiable functions, extreme value calculation – the Riemann integral

Die Vorlesung folgt dem Skript Analysis I

Weitere Literatur:

• Blatter, Ch.: Analysis 1. Springer-Verlag. xi+369 S.

• Forster, O.: Analysis 1. Differential- und Integralrechnung einer Veränderlichen. Vieweg. viii+208 S.

• Fritzsche, K.: Grundkurs Analysis 1 - Differentiation und Integration in einer Veränderlichen. Spektrum Verlag. xii+372 S.

Zahlreiche Beispiele, Anwendungen und nützliche Anmerkungen findet man in

• Arens, T. , et al.: Mathematik. Heidelberg: Spektrum Akademischer Verlag. 2008. xiv+1496 S.

Das folgende Buch entwickelt die Analysis aus dem historischen Kontext

• Körle, H.-H.: Die phantastische Geschichte der Analysis. Ihre Probleme und Methoden seit Demokrit und Archimedes. Dazu die Grundbegriffe von heute. Oldenbourg. 2009. xiv+217 S.