Students are able to think algorithmically. They know the basic static and dynamic data structures, the most important sorting and search algorithms, and the concepts of recursion and random numbers. Furthermore, they are able to analyze the complexity of algorithms with respect to runtime and memory requirements. In the exercises, students apply the topics presented in the lecture in practice by designing and implementing algorithms in Java.

Curriculum Bachelor's programme Computer Science: VL, opens an external URL in a new window, UE, opens an external URL in a new window
Language: German, Programming language: Java
Course id: VL 340100, UE 340110-34011x

Students are able to think algorithmically. They know the basic static and dynamic data structures, the most important sorting and search algorithms, and the concepts of recursion and random numbers. Furthermore, they are able to analyze the complexity of algorithms with respect to runtime and memory requirements. In the exercises, students apply the topics presented in the lecture in practice by designing and implementing algorithms in Python.

Curriculum Bachelor's programme Artificial Intelligence: VL, opens an external URL in a new window, UE, opens an external URL in a new window
Language: Englisch, Programming language: Python
Course id: VL 340200, UE 340210-34021x

Students are familiar with the most important advanced algorithms and data structures for searching based on trees and hashing. They understand the principle of graphs and learn how to implement graphs structures. Furthermore, students know advanced graph algorithms for finding paths and for analyzing flow networks. Additionally, they understand basic concepts of optimization using evolutionary and PRAM algorithms. Finally, students are able to create problem specific solutions, based on the knowledge, the transformation and the combination of approved algorithms. In the exercises, students apply the topics presented in the lecture in practice by designing and implementing algorithms in Java or Python.

Curriculum Bachelor's programme Computer Science: VL, opens an external URL in a new window, UE, opens an external URL in a new window
Course id: VL 340300, UE 34031x (Java), UE 34032x (Python)

Students learn general classifications, characteristics and applications of embedded systems. They know different sensors, actuators, possibilities of localization and positioning as well as methods of digital communication. Furthermore, students are able to analyze nonfunctional system characteristics and realtime systems and implement schedulers and concurrent models. In the exercises, students will gain hands on experience with embedded systems in three workshops. Students are able to apply the topics presented in the lecture in practice in form of assignments and a group project throughout the semester.

Curriculum Bachelor's programme Computer Science: VL, opens an external URL in a new window, UE, opens an external URL in a new window
Course id: VL 340400, UE 340410-34041x

Project Practical for students writing their bachelor thesis at the Institute of Pervasive Computing. During the Project Practical, the bachelor thesis is written and assessed.

Curriculum Bachelor's programme Computer Science: PR, opens an external URL in a new window
Course id: 340010

Course for students who plan to write their bachelor thesis in Artificial Intelligence at the Institute of Pervasive Computing. “Seminar in AI” (3 ECTS, 4th semester, Summer terms) and the “Practical Work in AI” (7.5 ECTS, 5th semester, Winter terms) should prepare the students for their Bachelor’s thesis (6th semester, Summer terms), although they are allowed to switch their subject again if they want.

Curriculum Bachelor's programme Artificial Intelligence: PR, opens an external URL in a new window
Course id: 340912

Course for students who plan to write their bachelor thesis in Artificial Intelligence at the Institute of Pervasive Computing. “Seminar in AI” (3 ECTS, 4th semester, Summer terms) and the “Practical Work in AI” (7.5 ECTS, 5th semester, Winter terms) should prepare the students for their Bachelor’s thesis (6th semester, Summer terms), although they are allowed to switch their subject again if they want.

Curriculum Bachelor's programme Artificial Intelligence: SE, opens an external URL in a new window
Course id: 340910

Course for students who write their bachelor thesis in Artificial Intelligence at the Institute of Pervasive Computing.

Curriculum Bachelor's programme Artificial Intelligence: PR, opens an external URL in a new window
Course id: 340915

The continuously unfolding field of Pervasive Computing refers to computing which is made to appear anytime and everywhere. The concept of using small internet-connected and inexpensive computers to help with everyday functions in an automated fashion touches on distributed computing, mobile computing, location computing, mobile networking, sensor networks, human-computer interaction, context-aware technologies, and artificial intelligence. This course focuses on the underlying state-of-the art of ubicomp technologies, the challenges it raises across computer science: in systems design and engineering, in systems modelling, and in user interface design, as well as the potential domains of application. The students should understand the fundamental underlying principles, methods and models essential for enabling a fully robust ubiquitous computing systems and environments. The exercises Pervasive Computing: Systems and Environments and Pervasive Computing: Design and Development are held together. The students will have the opportunity to work with the state-of-the-art of sensing technologies and practice various machine learning classification methods. The students should gain hands-on experience in all phases of pervasive system design and development and a practical understanding of the embedded computing time and space constraints.

Curriculum Master's programme Computer Science: VL, opens an external URL in a new window, UE, opens an external URL in a new window
Course id: VL 340500, UE 340510

The continuously unfolding field of Pervasive Computing refers to computing which is made to appear anytime and everywhere. This course focuses on the basics of developing pervasive computing systems, which goes way beyond the development of traditional computing systems, and thereby demands abstractions for computing ensembles, real time, real space, goal orientedness, dependability, correctness, modalities of interaction, explicit and implicit use, usability and trust. The students should become aware of the key design principles and development methods of the emerging "natural" interaction paradigm as opposed to traditional interactive models, e.g. command-line, menu-driven, or GUI-based. The exercises Pervasive Computing: Systems and Environments and Pervasive Computing: Design and Development are held together. The students will have the opportunity to work with the state-of-the-art of sensing technologies and practice various machine learning classification methods. The students should gain hands-on experience in all phases of pervasive system design and development and a practical understanding of the embedded computing time and space constraints.

Curriculum Master's programme Computer Science: VL, opens an external URL in a new window, UE, opens an external URL in a new window
Course id: VL 340600, UE 340610

Seminar for students who write their Master's thesis at the Institute for Pervasive Computing. Students present and discuss the progress of their Master's thesis. They learn to critically reflect on the presentations of other students and to give inputs during discussions.

Curriculum Master's programme Computer Science, SE-WS, opens an external URL in a new window, SE-SS, opens an external URL in a new window
Course id: 340020, 340021

By working on a non-trivial and coherent project from the area of the Major Subject, students should demonstrate their ability to apply the acquired knowledge from the Major Subject in a practical setting. During the semester, a project from the core subject is worked on independently or in a team.

Curriculum Master's programme Computer Science, PR, opens an external URL in a new window
Course id: 340030

Seminars guide students to do scientific work. In particular, they learn to work independently on a scientific topic in Pervasive Computing, to do literature research, to write a seminar report according to scientific standards, to give a presentation and to defend it and to discuss the presentations of other participants.

Curriculum Master's programme Computer Science, SE, opens an external URL in a new window
Course id: 340040

Selected former Seminar and Project Topics:

Seminar for students who write their master thesis in Artificial Intelligence at the Institute of Pervasive Computing.

Curriculum Master's programme Artificial Intelligence, SE, opens an external URL in a new window
Course id: 340920

Seminar for students who write their PhD thesis at the Institute of Pervasive Computing.

Curriculum Doctoral programme Engineering Sciences, SE, opens an external URL in a new window
LVA-Nr.: 340001