Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-74-51-M-6

Embedded Intelligence (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-74-51-M-6 Embedded Intelligence 4.0 CP (120 h)


CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in SuSe
Level [6] Master (General)
Language [EN] English
Module Manager
Area of study [INF-KI] Intelligent Systems
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-74-51-K-6
Embedded Intelligence
P 42 h 78 h
- PL1 4.0 SuSe
  • About [INF-74-51-K-6]: Title: "Embedded Intelligence"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-74-51-K-6]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.

Examination achievement PL1

  • Form of examination: written exam (Klausur) (60-180 Min.)
  • Examination Frequency: Examination only within the course
  • Examination number: 67499 ("Embedded Intelligence")

Evaluation of grades

The grade of the module examination is also the module grade.


This lecture focuses on the basic technologies of Embedded Intelligence, e.g., to acquire information from human beings and the environment, to build models with them and applications on top.
  • Questions, problems to be solved and examples
  • The attributes and application areas of different sensors
  • Different signal processing and machine learning methods for different activity recognition tasks
  • Keypoints to be considered in building the architecture for activity recognition
  • Dynamic sensor configuration
  • Performance evaluation

Competencies / intended learning achievements

Upon successful completion of the module, students will be able to
  • explain the basic concepts of embedded intelligence
  • solve simple activity detection tasks,
  • explain classes and examples of the problem using concrete applications,
  • analyze the characteristics and possible uses of different sensors with regard to different problems,
  • analyze the suitability of different methods of signal processing and machine learning for specific recognition tasks,
  • assess resource requirements in the recognition architectures,
  • evaluate solutions with dynamic sensor configurations,
  • construct examples of complete recognition architectures from concrete applications


Will be given during the lecture.

Requirements for attendance of the module (informal)

Knowledge in signal processing and machine learning

at least one programming language (C/C++, Java, MATLAB/Python)

Requirements for attendance of the module (formal)


References to Module / Module Number [INF-74-51-M-6]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.D55-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
Module-Pool Name
[EIT-AC-MSC-TW-MPOOL-7] General Elective Modules Master A&C
[GS-CVT-CS-2022-E-MPOOL-6] Catalog Electives Computer Science 2022
[GS-CVT-CS-E-MPOOL-6] Catalog Electives Computer Science