01-15-03-IoT(a)-V | Internet of Things (in englischer Sprache)
Vorlesung ECTS: 6 (4)
Einzeltermine: Mo 19.09.22 09:00 - 18:00 NW1 S1260 Mo 19.09.22 09:00 - 18:00 NW1 S1270 Mo 19.09.22 09:00 - 18:00 NW1 S1330 Mo 19.09.22 09:00 - 18:00 NW1 S1360 Di 20.09.22 09:00 - 18:00 NW1 S1270 Di 20.09.22 09:00 - 18:00 NW1 S1260 Di 20.09.22 09:00 - 18:00 NW1 S1330 Di 20.09.22 09:00 - 18:00 NW1 S1360 Mi 21.09.22 09:00 - 18:00 NW1 S1330 Mi 21.09.22 09:00 - 18:00 NW1 S1360 Mi 21.09.22 09:00 - 18:00 NW1 S1270 Mi 21.09.22 09:00 - 18:00 NW1 S1260 Do 22.09.22 09:00 - 18:00 NW1 S1330 Do 22.09.22 09:00 - 18:00 NW1 S1260 Do 22.09.22 09:00 - 18:00 NW1 S1270 Do 22.09.22 09:00 - 18:00 NW1 S1360 Fr 23.09.22 09:00 - 18:00 NW1 S1260 Fr 23.09.22 09:00 - 18:00 NW1 S1270 Fr 23.09.22 09:00 - 18:00 NW1 S1330 Fr 23.09.22 09:00 - 18:00 NW1 S1360 Mo 26.09.22 09:00 - 18:00 NW1 S1330 Mo 26.09.22 09:00 - 18:00 NW1 S1360 Mo 26.09.22 09:00 - 18:00 NW1 S1270 Mo 26.09.22 - Di 27.09.22 (Mo, Di) 09:00 - 18:00 NW1 S1260 Di 27.09.22 09:00 - 18:00 NW1 S1270 Di 27.09.22 09:00 - 18:00 NW1 S1330 Di 27.09.22 09:00 - 18:00 NW1 S1360 Mi 28.09.22 09:00 - 18:00 NW1 S1330 Mi 28.09.22 09:00 - 18:00 NW1 S1360 Mi 28.09.22 09:00 - 18:00 NW1 S1260 Mi 28.09.22 09:00 - 18:00 NW1 S1270 Do 29.09.22 09:00 - 18:00 NW1 S1260 Do 29.09.22 09:00 - 18:00 NW1 S1270 Do 29.09.22 09:00 - 18:00 NW1 S1330 Do 29.09.22 09:00 - 18:00 NW1 S1360 Fr 30.09.22 09:00 - 18:00 NW1 S1260 Fr 30.09.22 09:00 - 18:00 NW1 S1270 Fr 30.09.22 09:00 - 18:00 NW1 S1330 Fr 30.09.22 09:00 - 18:00 NW1 S1360
Blockkurs, findet in den Räumen S1260, S1270, S1330 und S1360 statt. Zeiten nach Absprache.
| Dr. Andreas Könsgen Prof. Dr. Anna Förster Dr. Asanga Udugama
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03-06-M-313 | Mathematics. Computer Science. Digital Media. Beginnings (in englischer Sprache)
Seminar ECTS: 6
Termine: wöchentlich Di 14:00 - 18:00 MZH 1110 Seminar
| Frieder Nake
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03-IBAP-CS (03-BB-711.01) | Cognitive Systems (in englischer Sprache) Grundlagen der Informationsverarbeitung in natürlichen und künstlichen Systemen
Vorlesung ECTS: 6
Termine: wöchentlich Di 08:00 - 10:00 GW2 B1410 Vorlesung Präsenz wöchentlich Mi 08:00 - 10:00 CART Rotunde - 0.67 Übung Präsenz wöchentlich Mi 10:00 - 12:00 CART Rotunde - 0.67 Übung Präsenz
| Thomas Dieter Barkowsky
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03-IBAP-ML (03-BB-710.10) | Grundlagen des Maschinellen Lernens (in englischer Sprache) Fundamentals of Machine Learning
Kurs ECTS: 6
Termine: wöchentlich Mo 14:00 - 16:00 MZH 3150 Übung Präsenz wöchentlich Mi 10:00 - 12:00 MZH 1380/1400 MZH 6200 Vorlesung Präsenz wöchentlich Mi 14:00 - 16:00 MZH 1100 Übung Präsenz
Einzeltermine: Mi 27.07.22 10:00 - 14:00 MZH 1380/1400 Mi 27.07.22 10:00 - 14:00 MZH 1470
Schwerpunkt: AI
| Tanja Schultz Felix Putze Mazen Salous Darius Ivucic Gabriel Ivucic
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03-IBAP-MRCA | Modern Robot Control Architectures (in englischer Sprache)
Vorlesung ECTS: 6
Termine: wöchentlich Mo 10:00 - 12:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Vorlesung Präsenz wöchentlich Do 14:00 - 16:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Übung Präsenz
Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks. This course aims to provide the fundamentals of modern robot control approaches that enable robotic agents to operate in the environment autonomously. The course introduces a basic understanding of autonomous robots, along with tools and methods to control various types of mobile robotic platforms and manipulators. Firstly, the course presents the types of sensors and actuators employed in autonomous robotic platforms. Secondly, it offers a formal understanding of the robot geometry, its kinematic and dynamic models. Finally, the course provides methods and approaches to control the robotic system from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations. Contents - Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
- Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
- Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
- Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
- Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
- Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
- Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A\*).
- Kinodynamic Planning: transcribing a dynamic planning problem into trajectory optimization, direct and indirect methods, costs and constraints.
- Reinforcement Learning-based Control: mathematical foundations, discrete vs continuous methods, reinforcement learning for closed-loop robot control.
- Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.
- Optimal Control: energy-shaping control, LQR and time-varying LQR control.
Learning Outcomes At the end of the course, the student is expected to be able to: - Define robot autonomy and list its key aspects.
- Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
- Implement and understand the low-level actuator control methods.
- Compute the 3D world coordinate transformations for rigid bodies.
- Apply the robot forward and inverse geometric model.
- Describe a robotic system based on its kinematic and dynamic properties.
- Use probabilistic methods for robot localization.
- Generate an optimal path for a mobile robot or manipulator using graph search methods.
- Plan a path taking into account the robot kinodynamic properties.
- Use reinforcement learning methods to control simple robotic systems.
- Apply dynamical and optimal control methods on robotic systems such that they are robust against disturbances.
- Assess the strengths and limitations of different control methods presented in the course.
- Identify open challenges in robotics research and current trends in state-of-the-art.
- Communicate confidently using the terminology in the field of robotics.
- Cooperate and work in teams in order to solve tasks.
Examination a) Submission of 6 worksheets in groups of 4 students and group interview for final grade (Übungsaufgaben und Fachgespräch). b) Individual oral exam without worksheet submission (mündliche Prüfung). References - Mechanics of Robotic Manipulation, Mathew T. Masen, MIT press, 2001.
- Algebra and Geometry, Alan F. Beardon, Cambridge University Press, 2005.
- Modelling and Control of Robot Manipulators, Lorenzo Sciavicco, Bruno Siciliano, Springer, 2000.
- Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), Sebastian Thrun, Wolfram Burgard, and Dieter Fox, MIT Press, 2005.
- Introduction to Autonomous Mobile Robots, Siegwart R., Nourbakhsh I., Scaramuzza D., MIT press, 2011.
- Automated Planning: Theory and Practice, Malik Ghallab, Dana Nau, Paolo Traverso, Elsevier, 2004.
- Behaviour-based robotics, R. C. Arkin, MIT press, 1998.
- Modern Robotics: Mechanics, Planning, and Control, Kevin M. Lynch and Frank C. Park, Cambridge University Press, 2017.
| Frank Kirchner M. Sc. Mihaela Popescu (Organizer)
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03-IBVA-DS (03-BE-802.98a) | Data Science (in englischer Sprache) Applied Machine Learning
Kurs ECTS: 6
Termine: wöchentlich Mo 16:00 - 18:00 Online Kurs online
From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.
The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks: • Classification (e.g. k-NN, Decision Trees, Support Vector Machines) • Regression (Linear Regression, Logistic Regression) • Clustering (k-means) • Dimensionality Reduction (PCA, t-SNE) We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.
This class is taught remotely. Every week, the lecturer will upload new material to this website. To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.
This is an online course, not a lecture that was filmed and put online. The course format was adapted to suit both the needs of the medium and the material.
We will meet regularly, but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.
| Prof. Dr. Hendrik Heuer Dr. Juliane Jarke
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03-IMAA-CTHCI | Current Topics in Human Computer Interaction (in englischer Sprache)
Kurs ECTS: 6
Termine: wöchentlich Mi 12:00 - 16:00 MZH 1470 Kurs Präsenz
Profil: DMI Schwerpunkt: IMA-DMI, IMA-VMC
| Prof. Dr. Tanja Döring Dr. Susanne Putze Dr. Dmitry Alexandrovsky
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03-IMAA-EC (03-MB-804.03) | Entertainment Computing (in englischer Sprache)
Vorlesung ECTS: 6
Termine: wöchentlich Di 12:00 - 16:00 GW2 B1820 Vorlesung und Übung Präsenz
Profil: DMI Schwerpunkt: IMK-DMI, IMA-VMC
| Prof. Dr. Rainer Malaka Dr. Dmitry Alexandrovsky
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03-IMAP-WCOMP (03-MB-799.01) | Wearable Computing (in englischer Sprache)
Vorlesung ECTS: 6
Termine: wöchentlich Mi 14:00 - 16:00 MZH 1090 wöchentlich Mi 16:00 - 18:00 MZH 1090
Profil: KIKR, DMI. Schwerpunkt: DMI, VMC, AI
| Dipl.-Inf. Alexej Wagner
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03-IMS-AIS | Seminar on Autonomous and Intelligent Systems (in englischer Sprache)
Seminar ECTS: 3
Termine: wöchentlich Di 16:00 - 18:00 DFKI RH1 B0.10 Seminar Präsenz
Einzeltermine: Di 19.07.22 14:00 - 18:00 RH1 B0.10
Profil: KIKR
| Frank Kirchner Melvin Laux
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03-IMS-APMSK (03-ME-711.09) | Ausgewählte Probleme der multisensorischen Kognition (in englischer Sprache) Selected Problems of Multisensory Cognition DIE VERANSTALTUNG ENTFÄLLT
Seminar ECTS: 3
Termine: wöchentlich Do 12:00 - 14:00 CART Rotunde - 0.67 Seminar Präsenz
Profil: KIKR, DMI. Die Veranstaltung findet in Englischer Sprache statt.
| Christop W. Zetzsche-Schill Kerstin Schill
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03-IMVA-ACSS | Applied Computer Science in Sports (in englischer Sprache)
Kurs ECTS: 6
Termine: wöchentlich Mi 14:00 - 16:00 MZH 6200 Vorlesung Präsenz wöchentlich Mi 16:00 - 18:00 MZH 6200 Übung Präsenz wöchentlich Mi 16:00 - 18:00 MZH 1100 Übung Präsenz
Einzeltermine: Mi 21.09.22 14:00 - 18:00 CART Rotunde - 0.67
Schwerpunkt: AI, DMI This aim of this course is to create an understanding of the major aspects of sports applications. The course is split into two parts: the first half has a classic lecture/tutorial style, whereas the second half will focus on the creation of individual sports applications.
The lectures will explain the necessary fundamentals, such as sensor technology, user feedback, and the conduction of empirical studies, along with a number of inspiring examples.
In the project part, own prototypes for sports applications are developed in small groups. The exact application as well as the technical implementation approach can be chosen freely. The final graded outcome of the course will be a small sports application about which a presentation has to be held and a documentation in a scientific paper style has to be written.
The course will be held in English.
Schwerpunkt: AI, DMI
| Robert Porzel Dr. Tim Laue Bastian Dänekas
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03-IMVA-EI (03-ME-899.03) | Embodied Interaction (in englischer Sprache)
Kurs ECTS: 6
Termine: wöchentlich Do 10:00 - 14:00 MZH 1450 Kurs Präsenz
Profil: DMI. Schwerpunkt: DMI, VMC
| Robert Porzel Prof. Dr. Rainer Malaka
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03-IMVP-HCIR (03-ME-712.04) | Human-Centered Interaction in Robotics (in englischer Sprache)
Vorlesung ECTS: 6
Termine: wöchentlich Mo 14:00 - 16:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Vorlesung Präsenz wöchentlich Mi 16:00 - 18:00 Extern RH 1 (DFKI-Gebäude) Raum B0.10 Übung Präsenz
Profil: KIKR Schwerpunkt: AI
| Frank Kirchner Dr. rer. nat. Teena Hassan
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09-60-M8/9-K | Social Movements: Power, Resistance, and Political Dynamics in the Age of Digital Media (in englischer Sprache)
Seminar
Termine: zweiwöchentlich (Startwoche: 2) Di 12:00 - 14:00 FVG O0150 (Seminarraum) (1 SWS) zweiwöchentlich (Startwoche: 2) Di 14:00 - 16:00 SH D1020 (1 SWS)
| Hannah-Marie Büttner
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09-60-M8/9-N | Human Rights in the Digital Age (in englischer Sprache)
Seminar
Termine: wöchentlich Di 14:00 - 16:00 GW1 B0100 (2 SWS)
Einzeltermine: Mi 20.04.22 13:00 - 15:00 Online Do 28.04.22 13:00 - 15:00 Online Di 07.06.22 16:00 - 18:00 SFG 2020 Di 14.06.22 16:00 - 18:00 GW2 B1216 Di 28.06.22 14:00 - 18:00 Online
| Dennis Redeker
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12-55-200 | Digital Literacy and Language Learning (in englischer Sprache) Media literacy and language learning
Seminar ECTS: 3
Termine: zweiwöchentlich (Startwoche: 1) Fr 13:00 - 16:30 GW2 B1632 (2 SWS)
| N. N.
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