Content:

Selection from the following topics:

• Enumeration (twelvefold way, inclusion-exclusion, double counting, recursions, generating functions, inversion, Ramsey's Theorem, asymptotic counting)
• Discrete Structures (graphs, set systems, designs, posets, matroids)
• Graph Theory (trees, matchings, connectivity, planarity, colorings)

• J. Matousek, J. Nesetril (2002/2007): An Invitation to Discrete Mathematics, Oxford University Press, Oxford/Diskrete Mathematik, Springer Verlag, Berlin, Heidelberg.
• L. Lovasz, J. Pelikan, K. Vesztergombi (2003): Discrete Mathemtics - Elementary and Beyond/Diskrete Mathematik, Springer Verlag, New York.
• N. Biggs (2004): Discrete Mathematics. Oxford University Press, Oxford.
• M. Aigner (2004/2007): Diskrete Mathematik, Vieweg Verlag, Wiesbaden/Discrete Mathemattics, American Mathematical Society, USA.
• D. West (2011): Introduction to Graph Theory. Pearson Education, New York.

Target group:

BMS students, Master and Bachelor students

Content:

Selection from the following topics:

• Counting (basics, double counting, Pigeonhole Principle, recursions, generating functions, Inclusion-Exclusion, inversion, Polya theory)
• Discrete Structures (graphs, set systems, designs, posets, matroids)
• Graph Theory (trees, matchings, connectivity, planarity, colorings)
• Algorithms (asymptotic running time, BFS, DFS, Dijkstra, Greedy, Kruskal, Hungarian, Ford-Fulkerson)

Contents:

Programming and synchronization of concurrent processes that share resources or interact through message passing.

• Non-Sequential programs and processes in their various forms, non-determinism, determinism
• Synchronization mechanisms: locks, monitors, guards, events, semaphores
• Non-Sequential program execution and object oriented systems
• Control flow, strategies selection, priorities, handling and avoiding deadlock
• Coroutines implementation, 
• - Multiprocessor systems
• Programming and Synchronisation of concurrent processes that interact through message passing
• Remote Calling Techniques
• Client-server, Peer-to-peer Networks
• Parallel computing over networks
• Concurrent and coordination languages
• Processing on the server and on the client.
• Middleware, structured communication, static and dynamic interfaces
• Event-based and stream-based processing
• Security of network applications
• Non-functional Aspects (time, memory, quality of service)

Literatur:

• Principles of Concurrent and Distributed Programming. M. Ben-Ari. Addison-Wesley. 
• Distributed Systems. Concepts and Design. Fifth Edition. George Coulouris, Jean Dollimore, Tim Kindberg, Gordon Blair. Pearson.

Contents:

Programming and synchronization of concurrent processes that share resources or interact through message passing.

• Non-Sequential programs and processes in their various forms, non-determinism, determinism
• Synchronization mechanisms: locks, monitors, guards, events, semaphores
• Non-Sequential program execution and object oriented systems
• Control flow, strategies selection, priorities, handling and avoiding deadlock
• Coroutines implementation, 
• - Multiprocessor systems
• Programming and Synchronisation of concurrent processes that interact through message passing
• Remote Calling Techniques
• Client-server, Peer-to-peer Networks
• Parallel computing over networks
• Concurrent and coordination languages
• Processing on the server and on the client.
• Middleware, structured communication, static and dynamic interfaces
• Event-based and stream-based processing
• Security of network applications
• Non-functional Aspects (time, memory, quality of service)

Literatur:

• Principles of Concurrent and Distributed Programming. M. Ben-Ari. Addison-Wesley. 
• Distributed Systems. Concepts and Design. Fifth Edition. George Coulouris, Jean Dollimore, Tim Kindberg, Gordon Blair. Pearson.

In the operating system section, students work on the fundamental structure of current operating systems and learn their basic tasks. They gain an understanding of the management of input/output systems and peripheral devices for networking, and practice programming DMA/PIO in C. Additionally, they explore the concepts of processes/threads, interrupts, and virtual memory, as well as memory management. They practice programming interrupt handling and memory management in C, and also learn the use of utilities such as shells. Furthermore, they become familiar with examples of operating systems (UNIX and Windows).

In the communication systems section, students work on the basic structure of networks, especially the Internet. They learn the TCP/IP protocol stack and the ISO/OSI reference model, discussing differences and applications. They become acquainted with media access methods and network devices, associating them with various layers of the protocol stack. They learn to handle transmission errors and discuss the limitations of these methods. Additionally, they learn routing methods within and outside Autonomous Systems, as well as TCP and UDP, and practice their implementation in C.

• Andrew S. Tanenbaum: 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.

Begleitveranstaltung zur Vorlesung 19300701

The module Mobile Communications covers many topics of mobile and wireless communication systems - an area of Computer Science that shows tremendous growth rates and that influences more and more areas of our society.

We put special emphasis on a system perspective throughout the whole module and give many references to real systems, international standards and up-to-date research results.

Topics coverd are:

• Basics of wireless transmission: frequencies, signals, antennas, signal propagation, multiplexing, modulation, spread spectrum, cellular systems;
• Media access: SDMA, FDMA, TDMA, CDMA;
• Wireless telecommunication systems: GSM, TETRA, UMTS, IMT-2000, LTE;
• Wireless local area networks: infrastructure/ad-hoc, IEEE 802.11/15, Bluetooth;
• Mobile networking: Mobile IP, ad-hoc networks;
• Mobile transport layer: traditional TCP, adapted TCP versions, additional mechanisms;
• Mobility support;
• Outlook: 5th generation mobile networks

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

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

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

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

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

Language

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

Credits & Exams

The criteria for gaining credits are

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

Website

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

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

Cybersecurity and AI is a four semester lecture cycle covering all main aspects of Cybersecurity and AI. This course teaches the fundamentals of cybersecurity attacks related to AI. It is in principle possible to start the lecture cycle at any semester.

The course outline is:

1. Introduction: AI & cybersecurity, adversarial threat model, evaluation metrics, attack examples
2. Robust AI: Definition and limits in deep learning, strengthening robustness, adversarial training, robust optimization methods
3. Adversarial Machine Learning: Defense mechanisms, transferability across domains, real-world implications
4. Generative AI: Generative models, deep fakes
5. Federated ML: Security challenges, secure aggregation protocols

Cybersecurity and AI ist ein viersemestriger Vorlesungszyklus, der alle wesentlichen Aspekte von Cybersecurity und KI abdeckt. Dieser Kurs vermittelt die Grundlagen für Angriffe auf- und mit KI. Grundsätzlich ist es möglich, den Vorlesungszyklus zu jedem Semester zu beginnen.

Der Lehrplan ist:

1.     Einführung: KI & Cybersicherheit, Bedrohungsmodell, Bewertungsmetriken, Beispiele für Angriffe
2.     Robuste KI: Definition und Grenzen des Deep Learning, Stärkung von Robustheit, robuste Optimierungsmethoden
3.     Adversarial ML: Verteidigungsmechanismen, Adversarial-Training, Übertragbarkeit, Real-World Implikationen
4.     Generative KI: Generative Modelle, Deepfakes
5.     Federated ML: Sicherheitsherausforderungen, sichere Aggregationsprotokolle

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

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

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

Language

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

Credits & Exams

The criteria for gaining credits are

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

Website

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

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

Cybersecurity and AI is a four semester lecture cycle covering all main aspects of Cybersecurity and AI. This course teaches the fundamentals of cybersecurity attacks related to AI. It is in principle possible to start the lecture cycle at any semester.

The course outline is:

1. Introduction: AI & cybersecurity, adversarial threat model, evaluation metrics, attack examples
2. Robust AI: Definition and limits in deep learning, strengthening robustness, adversarial training, robust optimization methods
3. Adversarial Machine Learning: Defense mechanisms, transferability across domains, real-world implications
4. Generative AI: Generative models, deep fakes
5. Federated ML: Security challenges, secure aggregation protocols

Cybersecurity and AI ist ein viersemestriger Vorlesungszyklus, der alle wesentlichen Aspekte von Cybersecurity und KI abdeckt. Dieser Kurs vermittelt die Grundlagen für Angriffe auf- und mit KI. Grundsätzlich ist es möglich, den Vorlesungszyklus zu jedem Semester zu beginnen.

Der Lehrplan ist:

1.     Einführung: KI & Cybersicherheit, Bedrohungsmodell, Bewertungsmetriken, Beispiele für Angriffe
2.     Robuste KI: Definition und Grenzen des Deep Learning, Stärkung von Robustheit, robuste Optimierungsmethoden
3.     Adversarial ML: Verteidigungsmechanismen, Adversarial-Training, Übertragbarkeit, Real-World Implikationen
4.     Generative KI: Generative Modelle, Deepfakes
5.     Federated ML: Sicherheitsherausforderungen, sichere Aggregationsprotokolle

Contents:

• models of computation
  • automata
  • formal languates
  • grammars and the Chomsky-hierarchy
  • Turing-machines
  • computabilty
• introduction to the complexity of computational problems

• Uwe Schöning, Theoretische Informatik kurzgefasst, 5. Auflage, Spektrum Akademischer Verlag, 2008
• John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman, Einführung in die Automatentheorie, Formale Sprachen und Komplexität, Pearson Studium, 3. Auflage, 2011
• Ingo Wegener: Theoretische Informatik - Eine algorithmenorientierte Einführung, 2. Auflage, Teubner, 1999
• Michael Sipser, Introduction to the Theory of Computation, 2nd ed., Thomson Course Technology, 2006
• Wegener, Kompendium theoretische Informatik - Eine Ideensammlung, Teubner 1996

Content

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

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

• Alfons Kemper, Andre Eickler: Datenbanksysteme - Eine Einführung, 5. Auflage, Oldenbourg 2004
• R. Elmasri, S. Navathe: Grundlagen von Datenbanksystemen, Pearson Studium, 2005

Requirements

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

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 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.

Further information can be found on the course website

Pony [1] is a programming language designed to be high performant.
It makes use of the actor paradigm [2]:
every sequential piece of code should be written as an actor
but the actors themselves run concurrently and independent from
each other.
Concurrency on the level of tasks [3] (in this case a task is an actor)
provides speed up by design.
However, there are other forms of parallelism on other levels
that could be exploited to make Pony even more performant.
Parallelism on data level [4] can for example be found in a loop nest
that executes many times on independent but equally structured data.

Graphic processing units (GPUs) specialize in computing data-level
parallel problems.[5]
In a system where a GPU complements a CPU data-level parallel portions
of code can be offloaded to the GPU.
The GPU takes care of calculating the data-level parallel problem
and uses its ressources to do this very fast.
This way a speed up of the overall program can be achieved.

In this software project we want to explore if GPU Offloading can be
integrated in the Pony Programming language.
In order to do that we take a look at the Pony compiler and experiment
with it and rewrite parts of it.

You can learn about the internal workings of a compiler
and how GPUs can be leveraged to achieve the speed up of a program.
Further, we will get to know more about
the compiler infrastructure LLVM [6] that the Pony compiler is a frontend of.
If you have never heard of LLVM before then maybe it is interesting for you to know
that the Rust and Clang compiler are other frontends to LLVM.

In its core this software project is a research project --
possibly with the option to later publish the results of our experiments
and contribute to the open source projects Pony and LLVM.

Links:

[1]: [Pony](https://www.ponylang.io/discover/#what-is-pony)
[2]: [Actor Model](https://en.wikipedia.org/wiki/Actor_model)
[3]: [Task-level parallelism](https://en.wikipedia.org/wiki/Task_parallelism)
[4]: David A. Patterson and John L. Hennessy. Computer Organization and Design: The Hardware Software Interface [RISC-V Edition]. 2nd. The Morgan Kaufmann Series in Computer Architecture and Design. Morgan Kaufmann, 2021. ISBN: 9780128203316. Page 528.
[5]: David A. Patterson and John L. Hennessy. Computer Organization and Design: The Hardware Software Interface [RISC-V Edition]. 2nd. The Morgan Kaufmann Series in Computer Architecture and Design. Morgan Kaufmann, 2021. ISBN: 9780128203316. Appendix B.
[6]: [LLVM](https://llvm.org/)

Target group

Bachelor and Master of Computer Science

Homepage

https://www.mi.fu-berlin.de/w/SE/SoftwareprojektCompilerOptimization2024

Lecturer & Contact Person

Barry Linnert

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 play the role of a software developer. In this role, students estimate the effort for requirements and implement them. Students must have prior software development experience.

Students will be organized into teams of 7-9 people, combining one Scrum master with two product owners with six 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.

Class consists of a 90 min. lecture followed by a 90 min. team meeting. Rooms and times for team meetings are assigned at the beginning of the semester. You must be able to regularly participate in the team meetings. If you can't, do not sign up for this course.

Sign-up and further course information are available through a Google spreadsheet at https://amos.uni1.de – please declare your interest by filling out the course interest declaration survey as soon as it opens.

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)

Grading

• Software developer
  • 10% of grade: 5 class quizzes, each consisting of 5 questions at 2 points each
  • 90% of grade: Weekly project work

Other

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

This course explores the concepts and techniques of continuous learning, an important area in machine learning. Continual learning, also known as lifelong learning, refers to the ability of AI systems to continuously learn and adapt to new information without losing previously acquired knowledge. This ability is crucial for the development of more flexible and adaptive AI systems, especially in rapidly changing and data-rich environments.

The course is for bio-informatics, computer science and data science students who want to develop a deeper understanding of advanced concepts in machine learning and gain hands-on experience in Continuous Learning.

,,

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In the scientific community, the practice of open source code and open
source code and open data as a standard for reproducibility and transparency.
established. In contrast, open hardware is still at an early stage of development.
an early stage of development. Researchers often develop their own hardware
hardware solutions are often developed - either for financial reasons or because
commercial products do not fulfil the specific requirements. These
developments are rarely recognised as fully-fledged research results and are
and are usually only mentioned in scientific publications as methodological
methodological components in scientific publications. In this project, we are investigating how an
open publication system can be designed to enable researchers to
researchers to publish their developments in the field of open hardware more effectively.
more effectively. Our aim is to develop a system that facilitates the easy
publication, peer review and verification of open hardware in scientific research.
scientific research.

Contents of the lecture

• General overview of computer graphics
• Basics of computer graphics
  • Points, lines, polygons, circles, ellipses
  • Filling areas, clipping and curves
• OpenGL and GLSL
  • Introduction to OpenGL with LWJGL
  • Shader programming in GLSL
  • Developing simple game environments
• Basics of lighting design
  • Perception of light, color blindness, barrier-free UIs
  • Goals of lighting design
  • Three- and four-point lighting, composition of a scene
• Local lighting models
  • Material properties, light source models
  • Elementary lighting models
  • Lighting models from Phong and OpenGL
• Shading models and visual perception
  • Flat and Gouraud shading
  • Neural networks and Machband effect, Phong shading
• Texture mapping
  • General and procedural
  • Perlin noise
• Normal mapping
• Geometric transformations
  • SD and 3D, homogeneous coordinates
  • Euler angles and quaternions
• Coordinate systems
  • World space, view space, clip space, MVP matrix
  • Tangential space, orthogonalization
• Microstructures with BRDF
  • Radiometry, Cartesian and polar coordinates
  • Solid angle, render equation
  • Derivation and investigation of BRDFs
• Displacement mapping
  • Per-vertex and per-pixel displacement mapping
• Real-time shadows
  • Hard, soft and filtered hard shadows
  • Shadow volumes, shadow mapping
  • Screen-Space-Ambient-Occlusion

Some of the topics are presented with program examples in Java, LWJGL, OpenGL and GLSL.
These tools are also provided for working on the exercises.

The necessary mathematical basics will be introduced if required.

Target Audience

B.Sc.-students in their fifth semester, M.Sc.-students in computer science, mathematics, physics, etc.,

Literatur zur Veranstaltung

[1] Block-Berlitz M.: "Warum sich der Dino furchtbar erschreckte - Lehrbuch zu Beleuchtung und Rendering mit Java,
     LWJGL, OpenGL, OpenCV und GLSL", vividus Wissenschaftsverlag, 3. Auflage, 2021

Ergänzende Literatur

[2] Birn J.: "Digital Lighting & Rendering", 3. Auflage, New Riders Verlag, 2015
[3] Foley J.D., et al.: "Computer Graphics: Principles and Practise", Addison-Wesley Verlag, 2. Auflage, 1997
[4] Shirley P., et al.: "Fundamentals of Computer Graphics", CRC Press, AK Peters, 3. Auflage, 2009
[5] Akenine-Möller T., et al.: "Real-Time Rendering", 3. Auflage, AK Peters, 2008
[6] Eisemann E., et al.: "Real-Time Shadows", CRC Press, AK Peters, 2012
[7] Gortler S. J.: "Founddations of 3D Computer Graphics", MIT Press, 2012
[8] Han JH: "3D Graphics for Game Programming", CRC Press, 2011
[9] Ammeraal L, et al.: "Computer Graphics for Java Programmers", Springer Verlag, 3. Auflage, 2017
[10] Olano M. et al.: "Real-Time Shading", AK Peters, 2002
[11] Shreiner M., et al.: "OpenGL Programming Guide: The Official Guide to Learning OpenGL", Addison-Wesley Verlag, 2007
[12] Angel E.: "Interactive Computer Graphics", Addison-Wesley Verlag, 4. Auflage, 2006

In der Vorlesung "Computergrafik" werden die vorgestellten Themen teilweise mit Programmbeispielen in
Java, LWJGL, OpenGL und GLSL vorgestellt [1]. Diese Beispiele liegen als Programmpaket für die Bearbeitung
und Ausführung in Eclipse bereit:

Diese Werkzeuge sind für die Bearbeitung der Übungsaufgaben ebenfalls vorgesehen. In vorheriger Absprache
können allerdings auch andere Werkzeuge, als die zuvor genannten, zum Einsatz kommen.

[1] Block-Berlitz M.: "Warum sich der Dino furchtbar erschreckte - Lehrbuch zu Beleuchtung und Rendering
mit Java, LWJGL, OpenGL, OpenCV und GLSL", vividus Wissenschaftsverlag, 3. Auflage, 2021

The lecture held by Dr. Andreas Wolf (from the Bundesdruckerei) He will give a broad overview of biometric processes and applications. He will also address the current issues with ePassports and new electronic identity cards.

The course aims to include:

• General structure of biometric systems
• Features of biometric modalities
• IT security and risk assessment
• Errors in biometric processes
• Fingerprinting
• Facial and iris recognition
• Speaker recognition and other modalities
• Standards
• ePassport

Next to the theoretical foundations of biometric modalities, the students are to develop the ability to assess the applicability of biometrics in various scenarios.

The course is scheduled to begin on Friday, April 15.

The lecture notes are available at

https://drive.google.com/drive/folders/0B7NhYbv9QewkRkk2WVRuM2Rqd00?usp=sharing

Webex Link zu der Veranstaltung:

Meeting-Link: https://fu-berlin.webex.com/fu-berlin/j.php?MTID=m2cc50d96918fcaeb09f3c36a264f4f29

Meeting-ID: 121 079 7504

Meeting-Password: mCwDw274PS8

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

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

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

Language

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

Credits & Exams

The criteria for gaining credits are

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

Website

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

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

The digital age is no longer about deciding whether to use software, but about deciding which to use. Usability, often an implicit rather than explicit requirement, significantly influences this decision. Achieving high usability and a positive user experience requires a deep understanding of user goals, hidden needs, and cognitive abilities.

This is where the study of human-computer interaction (HCI) comes in. HCI is a field of computer science that focuses on creating human-centered technologies. However, it's important to understand that usability is not inherent in software, nor can it be developed separately as a software feature at some point in time. Usability is always contextual, and understanding that context is critical.

Improving usability also means changing the entire software development process. The goal is to create software that, despite its complexity and wealth of information, is usable by the intended audience. To achieve this goal, we can select and apply different principles and methods depending on the development phase and project situation.

In this Human-Computer Interaction course, we will explore these principles and methods. You will learn how to

• Apply human-centered design methods to your development practice: We'll cover techniques for understanding and incorporating user needs and preferences into the design process.
• Study people and collect data about their activities: You'll learn how to collect and interpret qualitative and quantitative data about user behavior and preferences.
• Synthesize data into conceptual models that help you derive requirements: We'll teach you how to translate your findings into actionable design goals and requirements.
• Conceptualize, design, and prototype graphical user interfaces based on requirements: You'll get hands-on experience creating user interfaces that meet these requirements.
• Evaluate your prototypes (low-fidelity and high-fidelity) in studies: Finally, you'll learn how to conduct user testing sessions and iterate on your designs based on user feedback.

By the end of this course, you'll have a solid foundation in HCI principles and methodologies. You'll be equipped with the skills necessary to design and develop usable interfaces that create a positive user experience. This course will help you become adept at creating software that not only meets functional requirements but also provides a satisfying user experience.

Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., Elmqvist, N., & Diakopoulos, N. (2016). Designing the user interface: Strategies for effective human-computer interaction. Pearson.

Dix, A., Finlay, J., Abowd, G. D., & Beale, R. (2004). Human-computer interaction. Pearson Education.

Sharp, H., Rogers, Y., & Preece, J. (2019). Interaction design: Beyond human-computer interaction (5th ed.). Wiley.

The lecture will be held online only during summer term 2021.

For further information: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/summer_term_2021/index.html

Data science has experienced rapid growth in recent years, driven largely by advances in machine learning. This development has opened up new opportunities in a wide range of social, scientific, and technological fields. However, it has become increasingly clear that focusing solely on the statistical and numerical aspects of data science often overlooks social nuances and ethical considerations. The field of Human-Centered Data Science (HCDS) is emerging to fill this gap, combining elements of human-computer interaction, social science, statistics, and computational techniques.

HCDS emphasizes the fundamental principles of data science and its human implications. These include research ethics, privacy, legal frameworks, algorithmic bias, transparency, fairness, accountability, data provenance, reproducibility, user experience design, human computation, and the societal impact of data science.

By the end of this course, students will be expected to

• Apply human-centered design methods to data science practice, taking into account ethical concerns and privacy requirements.
• Construct a reproducible data science workflow.
• Understand and differentiate key terms such as bias, fairness, accountability, transparency, and interpretability.
• Apply measures, techniques, and frameworks to make their data science results interpretable in the context of human-centered explainable AI (HC-XAI).
• Enhance data science workflows with qualitative research approaches.
• Be aware of the existing measures, techniques, and approaches that help to reflect on data science practices.

Students will not only understand the core concepts, theories, and practices of HCDS, but also the multiple perspectives from which data can be collected and processed. In addition, students will gain insight into the potential societal implications of current technological advances. This course aims to equip students with the ability to apply data science techniques in a mindful and conscientious manner, taking into account human and societal contexts, resulting in more ethical, inclusive, and meaningful data-driven solutions.

Aragon, Cecilia, et al. "Developing a research agenda for human-centered data science." Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. 2016.

Baumer, Eric PS. “Toward Human-Centered Algorithm Design.” Big Data & Society, 4(2), Dec. 2017. 

Kogan, Marina, et al. "Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices." Companion of the 2020 ACM International Conference on Supporting Group Work. 2020. pp. 151-156.

The course is held in cooperation with Prof. Sabine Ammon (TU Berlin).

Content:

Students will learn to critically assess the relationship between technology and society and to analyze the interactions between technology and society from an ethical perspective. Furthermore, students will deal with the deconstruction of the concept of neutrality of technology and learn to critically assess it. At the same time, the environment will be taken as a stakeholder in its own right in order to consider the impact of technological applications from a sustainability perspective. The course will provide students with the necessary theoretical foundations stemming from both Computer Science (in particular AI and digital technologies) and Ethics. This knowledge will be put into practice and deepened through case-based projects carried out in interdisciplinary groups.

Knowledge:

• Acquiring an understanding of foundational concepts of Philosophy and Ethics of Artificial Intelligence (AI) and their application in research and practice in the field;
• acquiring an overview of the current ethical challenges in AI;
• transdisciplinary perspectives on these said challenges (including sociological, ecological, political, economic, cultural, historical issues, etc.).

Skills:

• Critical discussion and evaluation of various current perspectives among ethical debates in Philosophy and Ethics of AI;
• development of own argumentative positions based on the fundamental concepts of Philosophy and Ethics of AI (articulation of logical reasoning supported by examples);
• drafting of current and practical case studies in relation to contemporary societal challenges and assessment of these case studies through interdisciplinary perspectives.

Competencies:

• Ability to apply methods of interdisciplinary cooperation, specifically at the intersection of humanities/social sciences and natural/technical sciences;
• ability to discuss and integrate critical feminist, queer and anti-racist perspectives on science, i.e., how sociotechnical problems surrounding race, gender, sexuality, status, class, ability etc. relate to institutional structures of power and domination;
• effective self-management in a collaborative group setting.

The lecture held by Dr. Andreas Wolf (from the Bundesdruckerei) He will give a broad overview of biometric processes and applications. He will also address the current issues with ePassports and new electronic identity cards.

The course aims to include:

• General structure of biometric systems
• Features of biometric modalities
• IT security and risk assessment
• Errors in biometric processes
• Fingerprinting
• Facial and iris recognition
• Speaker recognition and other modalities
• Standards
• ePassport

Next to the theoretical foundations of biometric modalities, the students are to develop the ability to assess the applicability of biometrics in various scenarios.

The course is scheduled to begin on Friday, April 15.

The lecture notes are available at

https://drive.google.com/drive/folders/0B7NhYbv9QewkRkk2WVRuM2Rqd00?usp=sharing

Webex Link zu der Veranstaltung:

Meeting-Link: https://fu-berlin.webex.com/fu-berlin/j.php?MTID=m2cc50d96918fcaeb09f3c36a264f4f29

Meeting-ID: 121 079 7504

Meeting-Password: mCwDw274PS8

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

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

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

Language

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

Credits & Exams

The criteria for gaining credits are

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

Website

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

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

The digital age is no longer about deciding whether to use software, but about deciding which to use. Usability, often an implicit rather than explicit requirement, significantly influences this decision. Achieving high usability and a positive user experience requires a deep understanding of user goals, hidden needs, and cognitive abilities.

This is where the study of human-computer interaction (HCI) comes in. HCI is a field of computer science that focuses on creating human-centered technologies. However, it's important to understand that usability is not inherent in software, nor can it be developed separately as a software feature at some point in time. Usability is always contextual, and understanding that context is critical.

Improving usability also means changing the entire software development process. The goal is to create software that, despite its complexity and wealth of information, is usable by the intended audience. To achieve this goal, we can select and apply different principles and methods depending on the development phase and project situation.

In this Human-Computer Interaction course, we will explore these principles and methods. You will learn how to

• Apply human-centered design methods to your development practice: We'll cover techniques for understanding and incorporating user needs and preferences into the design process.
• Study people and collect data about their activities: You'll learn how to collect and interpret qualitative and quantitative data about user behavior and preferences.
• Synthesize data into conceptual models that help you derive requirements: We'll teach you how to translate your findings into actionable design goals and requirements.
• Conceptualize, design, and prototype graphical user interfaces based on requirements: You'll get hands-on experience creating user interfaces that meet these requirements.
• Evaluate your prototypes (low-fidelity and high-fidelity) in studies: Finally, you'll learn how to conduct user testing sessions and iterate on your designs based on user feedback.

By the end of this course, you'll have a solid foundation in HCI principles and methodologies. You'll be equipped with the skills necessary to design and develop usable interfaces that create a positive user experience. This course will help you become adept at creating software that not only meets functional requirements but also provides a satisfying user experience.

Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., Elmqvist, N., & Diakopoulos, N. (2016). Designing the user interface: Strategies for effective human-computer interaction. Pearson.

Dix, A., Finlay, J., Abowd, G. D., & Beale, R. (2004). Human-computer interaction. Pearson Education.

Sharp, H., Rogers, Y., & Preece, J. (2019). Interaction design: Beyond human-computer interaction (5th ed.). Wiley.

The lecture will be held online only during summer term 2021.

For further information: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/summer_term_2021/index.html

Data science has experienced rapid growth in recent years, driven largely by advances in machine learning. This development has opened up new opportunities in a wide range of social, scientific, and technological fields. However, it has become increasingly clear that focusing solely on the statistical and numerical aspects of data science often overlooks social nuances and ethical considerations. The field of Human-Centered Data Science (HCDS) is emerging to fill this gap, combining elements of human-computer interaction, social science, statistics, and computational techniques.

HCDS emphasizes the fundamental principles of data science and its human implications. These include research ethics, privacy, legal frameworks, algorithmic bias, transparency, fairness, accountability, data provenance, reproducibility, user experience design, human computation, and the societal impact of data science.

By the end of this course, students will be expected to

• Apply human-centered design methods to data science practice, taking into account ethical concerns and privacy requirements.
• Construct a reproducible data science workflow.
• Understand and differentiate key terms such as bias, fairness, accountability, transparency, and interpretability.
• Apply measures, techniques, and frameworks to make their data science results interpretable in the context of human-centered explainable AI (HC-XAI).
• Enhance data science workflows with qualitative research approaches.
• Be aware of the existing measures, techniques, and approaches that help to reflect on data science practices.

Students will not only understand the core concepts, theories, and practices of HCDS, but also the multiple perspectives from which data can be collected and processed. In addition, students will gain insight into the potential societal implications of current technological advances. This course aims to equip students with the ability to apply data science techniques in a mindful and conscientious manner, taking into account human and societal contexts, resulting in more ethical, inclusive, and meaningful data-driven solutions.

Aragon, Cecilia, et al. "Developing a research agenda for human-centered data science." Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. 2016.

Baumer, Eric PS. “Toward Human-Centered Algorithm Design.” Big Data & Society, 4(2), Dec. 2017. 

Kogan, Marina, et al. "Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices." Companion of the 2020 ACM International Conference on Supporting Group Work. 2020. pp. 151-156.

The course is held in cooperation with Prof. Sabine Ammon (TU Berlin).

Content:

Students will learn to critically assess the relationship between technology and society and to analyze the interactions between technology and society from an ethical perspective. Furthermore, students will deal with the deconstruction of the concept of neutrality of technology and learn to critically assess it. At the same time, the environment will be taken as a stakeholder in its own right in order to consider the impact of technological applications from a sustainability perspective. The course will provide students with the necessary theoretical foundations stemming from both Computer Science (in particular AI and digital technologies) and Ethics. This knowledge will be put into practice and deepened through case-based projects carried out in interdisciplinary groups.

Knowledge:

• Acquiring an understanding of foundational concepts of Philosophy and Ethics of Artificial Intelligence (AI) and their application in research and practice in the field;
• acquiring an overview of the current ethical challenges in AI;
• transdisciplinary perspectives on these said challenges (including sociological, ecological, political, economic, cultural, historical issues, etc.).

Skills:

• Critical discussion and evaluation of various current perspectives among ethical debates in Philosophy and Ethics of AI;
• development of own argumentative positions based on the fundamental concepts of Philosophy and Ethics of AI (articulation of logical reasoning supported by examples);
• drafting of current and practical case studies in relation to contemporary societal challenges and assessment of these case studies through interdisciplinary perspectives.

Competencies:

• Ability to apply methods of interdisciplinary cooperation, specifically at the intersection of humanities/social sciences and natural/technical sciences;
• ability to discuss and integrate critical feminist, queer and anti-racist perspectives on science, i.e., how sociotechnical problems surrounding race, gender, sexuality, status, class, ability etc. relate to institutional structures of power and domination;
• effective self-management in a collaborative group setting.

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

Furthermore, the lecture will delve into the application of data visualization in NLP, emphasizing the visualization of word embeddings and language models to aid in the exploration of semantic relationships between words and the interpretation of language model behavior. In the context of Explainable AI, the focus will be on using visualizations to explain model predictions and feature importance, thereby enhancing the interpretability of AI models. By leveraging the nested model of visualization and focusing on NLP and Explainable AI, the lecture aims to empower participants with the essential skills to design and validate advanced data visualizations tailored to these specific applications, ultimately enabling them to effectively communicate complex data patterns and gain deeper insights from their data.

Cybersecurity and AI is a four semester lecture cycle covering all main aspects of Cybersecurity and AI. This course teaches the fundamentals of cybersecurity attacks related to AI. It is in principle possible to start the lecture cycle at any semester.

The course outline is:

1. Introduction: AI & cybersecurity, adversarial threat model, evaluation metrics, attack examples
2. Robust AI: Definition and limits in deep learning, strengthening robustness, adversarial training, robust optimization methods
3. Adversarial Machine Learning: Defense mechanisms, transferability across domains, real-world implications
4. Generative AI: Generative models, deep fakes
5. Federated ML: Security challenges, secure aggregation protocols

Cybersecurity and AI ist ein viersemestriger Vorlesungszyklus, der alle wesentlichen Aspekte von Cybersecurity und KI abdeckt. Dieser Kurs vermittelt die Grundlagen für Angriffe auf- und mit KI. Grundsätzlich ist es möglich, den Vorlesungszyklus zu jedem Semester zu beginnen.

Der Lehrplan ist:

1.     Einführung: KI & Cybersicherheit, Bedrohungsmodell, Bewertungsmetriken, Beispiele für Angriffe
2.     Robuste KI: Definition und Grenzen des Deep Learning, Stärkung von Robustheit, robuste Optimierungsmethoden
3.     Adversarial ML: Verteidigungsmechanismen, Adversarial-Training, Übertragbarkeit, Real-World Implikationen
4.     Generative KI: Generative Modelle, Deepfakes
5.     Federated ML: Sicherheitsherausforderungen, sichere Aggregationsprotokolle

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

Furthermore, the lecture will delve into the application of data visualization in NLP, emphasizing the visualization of word embeddings and language models to aid in the exploration of semantic relationships between words and the interpretation of language model behavior. In the context of Explainable AI, the focus will be on using visualizations to explain model predictions and feature importance, thereby enhancing the interpretability of AI models. By leveraging the nested model of visualization and focusing on NLP and Explainable AI, the lecture aims to empower participants with the essential skills to design and validate advanced data visualizations tailored to these specific applications, ultimately enabling them to effectively communicate complex data patterns and gain deeper insights from their data.

Reproducing kernels provide a powerful modelling and approximation framework, which have been widely applied to problems in science and technology. Rooted in functional analysis and approximation theory, kernels lead to a theoretical framework of surprising mathematical elegance on one hand, and to simple and robust algorithms on the other hand. In this course, we will learn about both theory and applications of kernel methods. Practical coding exercises will accompany the course.
 

Theoretical Concepts
- concepts from functional analysis
- introduction to reproducing kernel Hilbert spaces; fundamental properties
- examples
- Mercer integral operator and feature representation
- density and regularity properties of reproducing kernel Hilbert spaces; relation to Fourier transform
- error analysis and statistical learning theory
 

Applications
- support vector machine
- kernel PCA
- canonical correlation analysis and analysis of dynamical systems
- solution of partial differential equations

This lecture/lab course is suitable for Master students of Mathematics, Computer Science or Computational Sciences.
 

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.

Wird noch bekannt gegeben.

Target Audience

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

Prerequisites

"Advanced Algorithms" or a similar class

Wird noch bekannt gegeben.

Target Audience

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

Prerequisites

"Advanced Algorithms" or a similar class

Reproducing kernels provide a powerful modelling and approximation framework, which have been widely applied to problems in science and technology. Rooted in functional analysis and approximation theory, kernels lead to a theoretical framework of surprising mathematical elegance on one hand, and to simple and robust algorithms on the other hand. In this course, we will learn about both theory and applications of kernel methods. Practical coding exercises will accompany the course.
 

Theoretical Concepts
- concepts from functional analysis
- introduction to reproducing kernel Hilbert spaces; fundamental properties
- examples
- Mercer integral operator and feature representation
- density and regularity properties of reproducing kernel Hilbert spaces; relation to Fourier transform
- error analysis and statistical learning theory
 

Applications
- support vector machine
- kernel PCA
- canonical correlation analysis and analysis of dynamical systems
- solution of partial differential equations

This lecture/lab course is suitable for Master students of Mathematics, Computer Science or Computational Sciences.
 

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.

Cybersecurity and AI is a four semester lecture cycle covering all main aspects of Cybersecurity and AI. This course teaches the fundamentals of cybersecurity attacks related to AI. It is in principle possible to start the lecture cycle at any semester.

The course outline is:

1. Introduction: AI & cybersecurity, adversarial threat model, evaluation metrics, attack examples
2. Robust AI: Definition and limits in deep learning, strengthening robustness, adversarial training, robust optimization methods
3. Adversarial Machine Learning: Defense mechanisms, transferability across domains, real-world implications
4. Generative AI: Generative models, deep fakes
5. Federated ML: Security challenges, secure aggregation protocols

Cybersecurity and AI ist ein viersemestriger Vorlesungszyklus, der alle wesentlichen Aspekte von Cybersecurity und KI abdeckt. Dieser Kurs vermittelt die Grundlagen für Angriffe auf- und mit KI. Grundsätzlich ist es möglich, den Vorlesungszyklus zu jedem Semester zu beginnen.

Der Lehrplan ist:

1.     Einführung: KI & Cybersicherheit, Bedrohungsmodell, Bewertungsmetriken, Beispiele für Angriffe
2.     Robuste KI: Definition und Grenzen des Deep Learning, Stärkung von Robustheit, robuste Optimierungsmethoden
3.     Adversarial ML: Verteidigungsmechanismen, Adversarial-Training, Übertragbarkeit, Real-World Implikationen
4.     Generative KI: Generative Modelle, Deepfakes
5.     Federated ML: Sicherheitsherausforderungen, sichere Aggregationsprotokolle

• vector space, basis and dimension;
• linear map, matrix and rank;
• Gauss-elimination and linear systems of equations;
• determinants, eigenvalues and eigenvectors;
• euclidean vector spaces and orthonormalization;
• principal component transformation;
• fundamentals of algebra and number theory

• Albrecht Beutelspacher. Mathe-Basics zum Studienbeginn: Survival-Kit Mathematik. Springer Vieweg, 2. Auflage, Wiesbaden 2016.
• Peter Hartmann. Mathematik für Informatiker. Springer Vieweg, 6. Auflage, Wiesbaden 2015.
• Gerald Teschl und Susanne Teschl. Mathematik für Informatiker - Band 1: Diskrete Mathematik und Lineare Algebra, Springer Vieweg, 4. Auflage, Berlin Heidelberg 2013.
• Thomas Westermann. Mathematik für Ingenieure. Springer Vieweg, 7. Auflage, Berlin Heidelberg 2015.
• Kurt-Ulrich Witt. Lineare Algebra für die Informatik. Springer Vieweg, 1. Auflage, Wiesbaden 2013.

Welcome to the university and in the exciting field of mathematics! You chose a challenging degree course, which offers you unlimited possibilities. Mathematics is the language of the universe, and studying it means to decipher the secrets behind everything, from the movement of the stars to the software in your smartphones.

In Analysis I you will get acquainted with the basics of mathematics, from natural numbers to more complex topics like differentiation and integration. From optimizing flows of traffic, to understanding financial markets, to developing new technologies -- the abilities that you gain here have real, tangible applications that go far beyond the lecture hall.

The importance of the tutorials and of Zentralübung can’t be stressed enough. In the tutorials you will not only discuss the exercises, but also learn how to explain mathematical concepts to others -- an essential ability in mathematics and beyond. In the Zentralübung we will discuss your questions informally and in an interactive way, which is necessary for a comprehensive understanding.

Good luck on your journey to success!

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

The "Seminar: Distributed Systems" covers both theoretical and practical topics from various projects of the working group "Dependable Distributed Systems". A task must be completed alone or in a group of a maximum of 2-3 students through literature research, evaluation and writing of a scientific report.

The seminar is assigned to various modules. Please find out in advance whether you can take the course in a module from your degree program

Topics this semester are:
- Blockchain - technologies in everyday life (PoW, PoS, offline usage)
- Tangle - technologies as an alternative to blockchain for IoT
- Could - Computing
- Consensus algorithms

Details will be discussed in the first meeting.
The seminar: Distributed Systems is held in German or English, depending on the needs of the students.

The "Seminar: Distributed Systems" covers both theoretical and practical topics from various projects of the working group "Dependable Distributed Systems". A task must be completed alone or in a group of a maximum of 2-3 students through literature research, evaluation and writing of a scientific report.

The seminar is assigned to various modules. Please find out in advance whether you can take the course in a module from your degree program

Topics this semester are:
- Blockchain - technologies in everyday life (PoW, PoS, offline usage)
- Tangle - technologies as an alternative to blockchain for IoT
- Could - Computing
- Consensus algorithms

Details will be discussed in the first meeting.
The seminar: Distributed Systems is held in German or English, depending on the needs of the students.

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.

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.

Short description:

The course is an introduction to the area of Artificial Intelligence and will introduce the basic ideas and techniques underlying the design of intelligent machines. By the end of this course, you will have learned how to build autonomous (software) agents that efficiently make decisions in fully informed, partially observable and adversarial settings as well as how to optimize actions in uncertain sequential decision making environments to maximize expected reward.

Syllabus:

• Informed search
• Uninformed search
• Adversarial search
• Local search and Optimization
• Constraint Satisfaction Problems
• Markov Decision Processes
• Reinforcement Learning

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (http://aima.cs.berkeley.edu/)
• Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (https://mitpress.mit.edu/books/reinforcement-learning-second-edition)

Prerequisites:

Basic knowledge in Mathematics and Algorithms & Datastructures.

Content

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

Wird bekannt gegeben.

Requirement

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

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 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.

Further information can be found on the course website

Pony [1] is a programming language designed to be high performant.
It makes use of the actor paradigm [2]:
every sequential piece of code should be written as an actor
but the actors themselves run concurrently and independent from
each other.
Concurrency on the level of tasks [3] (in this case a task is an actor)
provides speed up by design.
However, there are other forms of parallelism on other levels
that could be exploited to make Pony even more performant.
Parallelism on data level [4] can for example be found in a loop nest
that executes many times on independent but equally structured data.

Graphic processing units (GPUs) specialize in computing data-level
parallel problems.[5]
In a system where a GPU complements a CPU data-level parallel portions
of code can be offloaded to the GPU.
The GPU takes care of calculating the data-level parallel problem
and uses its ressources to do this very fast.
This way a speed up of the overall program can be achieved.

In this software project we want to explore if GPU Offloading can be
integrated in the Pony Programming language.
In order to do that we take a look at the Pony compiler and experiment
with it and rewrite parts of it.

You can learn about the internal workings of a compiler
and how GPUs can be leveraged to achieve the speed up of a program.
Further, we will get to know more about
the compiler infrastructure LLVM [6] that the Pony compiler is a frontend of.
If you have never heard of LLVM before then maybe it is interesting for you to know
that the Rust and Clang compiler are other frontends to LLVM.

In its core this software project is a research project --
possibly with the option to later publish the results of our experiments
and contribute to the open source projects Pony and LLVM.

Links:

[1]: [Pony](https://www.ponylang.io/discover/#what-is-pony)
[2]: [Actor Model](https://en.wikipedia.org/wiki/Actor_model)
[3]: [Task-level parallelism](https://en.wikipedia.org/wiki/Task_parallelism)
[4]: David A. Patterson and John L. Hennessy. Computer Organization and Design: The Hardware Software Interface [RISC-V Edition]. 2nd. The Morgan Kaufmann Series in Computer Architecture and Design. Morgan Kaufmann, 2021. ISBN: 9780128203316. Page 528.
[5]: David A. Patterson and John L. Hennessy. Computer Organization and Design: The Hardware Software Interface [RISC-V Edition]. 2nd. The Morgan Kaufmann Series in Computer Architecture and Design. Morgan Kaufmann, 2021. ISBN: 9780128203316. Appendix B.
[6]: [LLVM](https://llvm.org/)

Target group

Bachelor and Master of Computer Science

Homepage

https://www.mi.fu-berlin.de/w/SE/SoftwareprojektCompilerOptimization2024

Lecturer & Contact Person

Barry Linnert