Contents

• foundations of computability:
  • universal register machines
  • syntax and operational semantics of imperative programming languages
• formal methods for the specification and verification of imperative programs:
  • assertions,
  • Hoare-calculus, partial correctness, termination
• imperative and object-oriented programming concepts (Java):
  • primitive and composite data types,
  • methords (procedures and functions). calling conventions, overloading
  • modules, classes, objects
  • class hierarchies, inheritance, polymorphism
  • abstract classes, interfaces
• programming methods:
  • stepwise refinement
  • divide and conquer
  • backtracking
• analysis of time and space requirements:
  • O-notation
  • recursion and iteration
  • analysis of algorithms for sorting and searching
  • algorithms, data structures, data abstraction

• Concepts of Programming Languages, Robert Sebesta, Pearson Education , 10th Edition, 2012, ISBN: 0131395319
• Data Structures & Problem Solving Using Java, Mark Allen Weiss, Addison Wesley, 4. Auflage, 2010, ISBN: 0-321-54140-5
• Cormen, Leiserson, Rivest: Introduction to Algorithms, 3. Auflage 2009,
• Bundle of algorithms in java, third edition, parts 1-5. Sedgewick Robert und Michael Schidlowsky. Addison-Wesley Longman, Amsterdam. 2003.

Prerequisites

background as taught in the class "Functional Programming".

Please also sign up for the class in our KVV.

Website: https://www.mi.fu-berlin.de/w/SE/VorlesungNichtseq_Vert_Prg2022

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.

Website: https://www.mi.fu-berlin.de/w/SE/VorlesungNichtseq_Vert_Prg2022

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.

The module operating and communication systems closes the gap between the hardware of a computer and the applications.

We will cover the following topics::

• I/O systems
• DMA/PIO
• Interrupt handling
• Buffers
• Processes/threads
• Virtual memory
• UNIX and Windows
• Shells
• Utilities
• Peripherals and networking
• Networks
• Media
• Media access
• Protocols
• Reference models
• TCP/IP
• The Internet

• 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.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

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.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

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

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.

Please refer to the German descrption.

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

Contents

This software project is about implementing and visualizing graph algorithms. The students are split up into small groups. Each group implements

• an algorithm or a group of algorithms,
• the necessary graph data structures, or
• the graphical user interface to visualize and create graphs.

Prerequsitions

Basic knowledge in design and anaylsis of algorithms.

je nach Anwendungsgebiet

The software project Distributed Systems is assigned to various different modules and contains a range of topics from the research area of the dependable distributed systems group. The projects aim at working on a more complex task containing design, implementation, and validation in a team.

Topics this semester are:

- development of a symptom checker to support the arrival of patients at the emergency unit of Charité Berlin. The project will be in a team with students from the medical school.
- development of tool support for laser welding in collaboration with Bundesanstalt für Materialforschung
- development of an app for federated learning
- development of a private blockchain to store welding quality data
-  your topic?

Details will be discussed in the first meeting.

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

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

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

Topic of the software project in SS22: Development of a demonstrator for AI methods for the detection of online hatspeech.

Online hatspeech is a growing challenge for society as a whole. Due to the increasing use of social networks as a communication channel and as a result of the amount of user-generated content, manual or classic processing of the information contained is impossible. This is why artificial intelligence methods are increasingly being used to automate moderation tasks. In this software project, AI methods (semantic technologies and machine learning) are examined and used to analyze and classify content in social media for legal, linguistic and sociological aspects of hate speech. The aim of this project is to create a basis for in-depth analyzes of hate speech and its content with regard to topics and acting and affected groups in order to be able to derive strategies for dealing with hate speech.

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.

Within the scope of the SWP Data Management, students learn how to independently plan and realize a software project under ( as close as possible) real-life conditions.

Wird bekannt gegeben

Requirement

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

Today’s supercomputers especially cluster computers are used for a wide range of applications – from common Client-Server-Apps to High Performance Computing. To support all of these different applications with their different requirements is a challenge for the management systems. That’s why there are different ongoing research projects in this field. This project seminar deals with enhancements of the management systems used to manage the different computer resources. Different applications and scenarios of usage should be supported transparently and in a reliable way. The enhancement will be based on previous work in the context of the Virtual Resource Manager (VRM) project.

Target group

Bachelor and Master of Computer Science

Language

The course language is German.

Homepage

https://www.mi.fu-berlin.de/w/SE/SoftwareprojektClusterManagement2022

Lecturer & Contact Person

Barry Linnert

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).

Automated methods for the recognition and analysis of behavior have been increasingly used to investigate individual and collective animal behavior. This includes applications such as tracking individuals in large groups (humans in crowds, animals in herds/swarms/flocks, cars in traffic), mapping observations of an individual to a behavioral space ("what is person X doing"), and learning about the structure of social interactions ("who is doing what with whom"). Recent methods allow processing large amounts of data resulting in surprisingly accurate results. We will look at a variety of supervised and unsupervised approaches with examples from our own research.

,,

,,

The goal is gaining hands-on experience on Artificial Intelligence/Machine Learning methods and algorithms in the context of a semester-long team project.

The projects fall into two categories: i) Academic projects which are typically based on some published papers. The goal is to reproduce the authors results with some extensions and application to new datasets. ii) Application-specific projects referring to the analysis of interesting datasets from different application domains.

Prerequisites: The project is intended for Master's students only. You should have attented (and passed) some Artificial Intelligence / Machine Learning courses. Since the course focuses on practical skills, strong programming skills are required.

This software project introduces students to Open Source Software (OSS) development projects. Students gain practical development experience by participating in existing OSS projects.

In the introductory phase, we give students an overview of OSS working practices, introduce existing OSS projects, and provide insights into the requirements of distributed software development projects. After the introductory phase, students apply an agile software development process and implement features in two-week sprints.

They are responsible for planning, managing, and implementing all defined features in a self-organized manner. At the end of the course, the students are able to:

• use git for planning the project, collecting features, coordinating with team members, and managing the development progress,  
• design, develop and test software in a real-world use case,
• organize and conduct presentations, and
• document the project results.

Through project-related learning, the participants can expand their communication skills, solve problems/tasks in a complex environment, and develop their technical skills. However, the participants do not remain alone during their development sprints. In regular meetings, we are available as consultants, mentors, and mediators. Additionally, we impart all the necessary methods and competencies if necessary.

Literature, materials and equipment will be provided during the event.

Link to the course on the HCC-Website: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/Summer-Term-2022/swp-coding-openness.html

Contents

• mathematical foundations of computer graphics,
• representation of 3d scenes in a comuter
• geometric transformations, projections,
• hidden surface removal, lighting,
• raytracing
• radiosity,
• animation

Target Audience

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

• Computer Graphics (2nd Edition C Version). D. Hearn, M.P. Baker, Prentice-Hall, 1997.
• Computer Graphics - Principles and Practice (2nd Edition in C). J.D. Foley, A. Van-Dam, S.K. Feiner and J.F. Hughes. Addison-Wesley, 1996
• Interactive Computer Graphics (4th Edition).E. Angel, Addison-Wesley, 2006
• OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 2.1.D. Shreiner, M. Woo, J. Neider, T. Davis, Addison-Wesley, 2007

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.

Das Vorlesungsskript liegt unter

https://drive.google.com/drive/folders/0B7NhYbv9QewkRkk2WVRuM2Rqd00?resourcekey=0-Yshu3zWsEGEP1i2z0UZjXw&usp=sharing

Webex Link zu der Veranstaltung (als Fallback bei notwendigem Wechsel zum virtuellen Format):
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.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

Both in the business environment and for private use, the question nowadays is no longer whether software should be used, but which software to choose. In such decision processes, the usability of the software is often not an explicit requirement, but implicitly it has a high influence on the selection decision. Excellent usability and positive user experience can only be achieved by knowing and understanding the user's goals, their hidden needs, and also their cognitive abilities. In computer science, we address these challenges in the research area of Human-Computer Interaction (HCI): Besides choosing the "right" technology, considering the users and their needs is fundamental for the later success of the service or product. However, usability is neither a characteristic inherent in software nor can usability, as a software feature, be developed separately at a certain point in time. Usability always stands in a particular usage context, and this context must be clearly understood. Furthermore, in order to improve usability, it is necessary to adapt the entire software development process. The goal should be to ensure that the software is suitable for use by the respective target group despite its complex functionality and wealth of information. Depending on the development phase, and the project situation, we can select and apply different principles and methods in our development process to reach that goal. In the course "Human-Computer Interaction I", you learn and apply these principles and methods to create a positive user experience.

Shneiderman, Ben, et al. "Designing the user interface: strategies for effective human-computer interaction". Pearson, 2016.

Dix A, Finlay J, Abowd GD, Beale R "Human-Computer Interaction". Pearson Prentice Hall 2004.

Sharp, Helen, Yvonne Rogers, and Jenny Preece. "Interaction design: beyond human-computer interaction." 2007.

For further information: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/Summer-Term-2022/human-computer-interaction_1.html

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. The course also teaches effective oral, written, and visual scientific communication as well as skills to reflect on the societal impacts of data science.

At the end of the course we expect students be able to

• apply human-centered design methods in the data science practice, including ethical concerns and privacy requirements,
• build reproducible data science workflow,
• know how to differentiate the terms bias, fairness, accountability, transparency and explanations,
• apply measures, techniques and frameworks on human-centered explainable AI (XAI),
• responsibly integrate human labor, i.e., crowdsourcing, in data science projects, and
• augment data science workflows by qualitative research approaches.

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.

Please note, 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, 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. http://doi.org/10.1145/2818052.2855518

Baumer, Eric PS. “Toward Human-Centered Algorithm Design.” Big Data & Society, 4(2), Dec. 2017. http://doi.org/10.1177/2053951717718854.

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. https://doi.org/10.1145/3323994.3369898

Link to the course on the HCC-Website:

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/Summer-Term-2022/course_human_centered_data_science.html

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.

Das Vorlesungsskript liegt unter

https://drive.google.com/drive/folders/0B7NhYbv9QewkRkk2WVRuM2Rqd00?resourcekey=0-Yshu3zWsEGEP1i2z0UZjXw&usp=sharing

Webex Link zu der Veranstaltung (als Fallback bei notwendigem Wechsel zum virtuellen Format):
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.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

Both in the business environment and for private use, the question nowadays is no longer whether software should be used, but which software to choose. In such decision processes, the usability of the software is often not an explicit requirement, but implicitly it has a high influence on the selection decision. Excellent usability and positive user experience can only be achieved by knowing and understanding the user's goals, their hidden needs, and also their cognitive abilities. In computer science, we address these challenges in the research area of Human-Computer Interaction (HCI): Besides choosing the "right" technology, considering the users and their needs is fundamental for the later success of the service or product. However, usability is neither a characteristic inherent in software nor can usability, as a software feature, be developed separately at a certain point in time. Usability always stands in a particular usage context, and this context must be clearly understood. Furthermore, in order to improve usability, it is necessary to adapt the entire software development process. The goal should be to ensure that the software is suitable for use by the respective target group despite its complex functionality and wealth of information. Depending on the development phase, and the project situation, we can select and apply different principles and methods in our development process to reach that goal. In the course "Human-Computer Interaction I", you learn and apply these principles and methods to create a positive user experience.

Shneiderman, Ben, et al. "Designing the user interface: strategies for effective human-computer interaction". Pearson, 2016.

Dix A, Finlay J, Abowd GD, Beale R "Human-Computer Interaction". Pearson Prentice Hall 2004.

Sharp, Helen, Yvonne Rogers, and Jenny Preece. "Interaction design: beyond human-computer interaction." 2007.

For further information: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/Summer-Term-2022/human-computer-interaction_1.html

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. The course also teaches effective oral, written, and visual scientific communication as well as skills to reflect on the societal impacts of data science.

At the end of the course we expect students be able to

• apply human-centered design methods in the data science practice, including ethical concerns and privacy requirements,
• build reproducible data science workflow,
• know how to differentiate the terms bias, fairness, accountability, transparency and explanations,
• apply measures, techniques and frameworks on human-centered explainable AI (XAI),
• responsibly integrate human labor, i.e., crowdsourcing, in data science projects, and
• augment data science workflows by qualitative research approaches.

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.

Please note, 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, 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. http://doi.org/10.1145/2818052.2855518

Baumer, Eric PS. “Toward Human-Centered Algorithm Design.” Big Data & Society, 4(2), Dec. 2017. http://doi.org/10.1177/2053951717718854.

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. https://doi.org/10.1145/3323994.3369898

Link to the course on the HCC-Website:

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/Summer-Term-2022/course_human_centered_data_science.html

In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.

This lecture deals with current research directions in the field of algorithms and complexity that are being investigated at the theory group of our department. Since the contents are adapted to the current topics, it cannot be determined in advance.

Wird noch bekannt gegeben.

Recommended previous knowlege:

Certificate in Higher Algorithms or similar

Target group:

Master students of Computer Science or Maths in advanced semesters of the BSc.

The lecture aims at a deeper understanding of cryptographic algorithms, especially which design criteria have to be considered for the development of secure encryption algorithms. For that purpose we will get to know and evaluate different cryptanalytic methods for symmetrical encryption techniques – e.g. linear and differential cryptanalysis on block ciphers, correlation attacks on stream ciphers etc. Weaknesses in the implementation, e.g. to exploit side-channel attacks, will be discussed only peripherally.

In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.

In this lecture, the information-theoretical foundations of identity and privacy are provided. The framework is used to derive basic performance limits of access control systems, biometric authentication, privacy preserving data release mechanisms, generation of synthetic databases, etc. The lecture is a good supplement to lecture 19302801 "Applied Biometry".

The outline is as follows:

1) Introduction: Access control systems and biometrics, information-theoretic tools

2) Secret sharing with privacy constraints and impostors

3) Biometric authentication, zero leakage, generated and chosen secrets

4) Biometric identification, secret binding

5) Practical constraints, fuzzy commitment

6) Data bases, syntectic data, information-theoretic tools

7) Information-theoretic differential privacy

8) Laplace and Exponential Mechanism, Composition

9) Differential privacy and rate distotion, information bottleneck approach

10) Practical constraints, Google RAPPOR

11) Systems with learning (variational autoencoder concepts)

In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.

In this lecture, the information-theoretical foundations of identity and privacy are provided. The framework is used to derive basic performance limits of access control systems, biometric authentication, privacy preserving data release mechanisms, generation of synthetic databases, etc. The lecture is a good supplement to lecture 19302801 "Applied Biometry".

The outline is as follows:

1) Introduction: Access control systems and biometrics, information-theoretic tools

2) Secret sharing with privacy constraints and impostors

3) Biometric authentication, zero leakage, generated and chosen secrets

4) Biometric identification, secret binding

5) Practical constraints, fuzzy commitment

6) Data bases, syntectic data, information-theoretic tools

7) Information-theoretic differential privacy

8) Laplace and Exponential Mechanism, Composition

9) Differential privacy and rate distotion, information bottleneck approach

10) Practical constraints, Google RAPPOR

11) Systems with learning (variational autoencoder concepts)

This lecture deals with current research directions in the field of algorithms and complexity that are being investigated at the theory group of our department. Since the contents are adapted to the current topics, it cannot be determined in advance.

Wird noch bekannt gegeben.

Recommended previous knowlege:

Certificate in Higher Algorithms or similar

Target group:

Master students of Computer Science or Maths in advanced semesters of the BSc.

The lecture aims at a deeper understanding of cryptographic algorithms, especially which design criteria have to be considered for the development of secure encryption algorithms. For that purpose we will get to know and evaluate different cryptanalytic methods for symmetrical encryption techniques – e.g. linear and differential cryptanalysis on block ciphers, correlation attacks on stream ciphers etc. Weaknesses in the implementation, e.g. to exploit side-channel attacks, will be discussed only peripherally.

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

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

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

Literature:

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

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

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

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

Content:

• Basic terms/concepts: sets, maps, equivalence relations, groups, rings, fields
• Linear equation systems: solvability criteria, Gauss algorithm
• Vector spaces: linear independence, generating systems and bases, dimension, subspaces, quotient spaces, cross products in R^3,
• 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 matrices, 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

• Whiteboard: Signing up for the course with the FU Whiteboard system is mandatory for participating. Whiteboard will also serve as a course homepage.
• Active participation: A problem set will be published every week. Each problem set has to be solved within one week. Submissions are allowed by groups of max. two students. For active participation, 50% of the total possible amount of points have to be reached. Moreover, we expect active participation in the tutorials.
• Regular participation: Participating in the lectures is recommended. Participation in the tutorials is required.
• Covid: The modus operandi of the course (online, hybrid, presence) will be determined at the beginning of the semester depending on the Covid situation. Please follow the Whiteboard homepage and announcements.

• linear algebra:
  • 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;
  • Applications of linear algebra in affine geometry, statistics, and coding theory  (linear codes)

• Klaus Jänich: Lineare Algebra, Springer-Lehrbuch, 10. Auflage 2004
• Dirk Hachenberger: Mathematik für Informatiker, Pearson 2005
• G. Grimmett, D. Welsh: Probability - An Introduction, Oxford Science Publications 1986
• Kurt Meyberg, Peter Vachenauer: Höhere Mathematik 1, Springer-Verlag, 6. Auflage 2001
• G. Berendt: Mathematik für Informatiker, Spektrum Akademischer Verlag 1994
• Oliver Pretzel: Error-Correcting Codes and Finite Fields, Oxford Univ. Press 1996

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

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

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

Literature:

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

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

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

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