Every semester
10 Credit points
For information on the duration of the modul, refer to the courses of study in which the module is used.
In this module, students acquire advanced application-related knowledge of technical methods from the fields of artificial intelligence and machine learning, which are necessary for the implementation of intelligent, adaptive behavior and the interaction capability of technical systems. Upon completion of the module, students will be able to independently develop models of AI/machine learning (e.g., deep learning, reinforcement learning, probabilistic models, XAI) and apply them to new contexts. In the first module part, applied-methodological knowledge on one of the topics of Applied Artificial Intelligence is acquired and practiced in accompanying exercises. In the second module part, these methods are practically deepened in two applied seminars or a project. For this purpose, students will have acquired both methodological knowledge and the competence of independent practical examination of a topic (research, evaluation, implementation, oral presentation, and written discussion).
The module teaches practical-applied content needed to develop intelligent interactive systems. The teaching content of the module includes e.g. courses from the areas of machine learning, artificial intelligence, deep learning, reinforcement learning, XAI, cognitive computing, models of decision-making, neural networks, auditory data science, interactive and autonomous learning. The courses chosen by the student determine the specific course content of the module. Selection from the range of courses designated for this purpose will be based on personal interest.
—
—
The following combinations of courses are permitted:
Depending on the chosen option, a course achievement must be completed in the Seminar 2 OR the Project.
Regardless of the option chosen, one graded (partial examination 1) and one ungraded (partial examination 2) examination must be completed. The type of examination depends on the chosen option.
Reasoning of the necessity of two partial exams:
Two partial examinations are necessary since the theoretical and mathematical competencies are tested in the written/oral examination and practical and methodological knowledge in the project/second seminar.
Module structure: 1 SL, 1 bPr, 1 uPr 1
To study together with the accompanying exercise from the field of Applied Artificial Intelligence.
To study together with the accompanying exercise from the field of Applied Artificial Intelligence.
To study together with a corresponding lecture or seminar from the field of Applied Artificial Intelligence.
To study together with a corresponding lecture or seminar from the field of Applied Artificial Intelligence.
To study together with a lecture/a seminar and the corresponding exercise from the field of Applied Artificial Intelligence.
| Allocated examiner | Workload | LP2 |
|---|---|---|
|
Teaching staff of the course
Applied Artificial Intelligence (focus): Project
(project)
Oral presentation on the implementation and results of the project work with a duration of 10 to 15 minutes. |
see above |
see above
|
|
Teaching staff of the course
Applied Artificial Intelligence (focus): application-oriented seminar 1
(seminar)
Oral presentation on a topic agreed upon with the examiner, lasting 30 to 40 minutes. |
see above |
see above
|
Partial Examination 1: (Lecture + Exercise) OR (Seminar + Exercise)
Portfolio with final examination consisting of:
1) Portfolio of exercises related to the content of the lecture or seminar
Exercise tasks or programming tasks that are assigned in relation to the course (passing threshold: 50% of the achievable points). The assessment of the exercise tasks also includes direct questions regarding the solutions that must be answered by the students during the exercises. The instructor may require an individual explanation and demonstration of tasks and can replace a portion of the exercise tasks with in-person exercises. The exercise tasks within the portfolio are generally assigned weekly and serve to support the independent learning of implementations of the content presented in the lecture.
2) A final examination for the lecture
The final examination regarding the content of the lecture refers to the exercise or programming tasks or develops from the competencies learned in the exercises. Further specification, particularly regarding the time frame of the final examination, will be provided in the course description.
Lecture: Final exam (lasting 90-120 minutes) or oral final examination (lasting 20-30 minutes) covering the content conveyed in the lecture and developed in the exercises.
The exam can alternatively be conducted as an e-exam, open book exam, or e-open book exam. In the case of open book and e-open book exams, the duration is 120-150 minutes.
OR
2) A final examination for the seminar
The final examination regarding the content of the seminar refers to the exercise or programming tasks or develops from the competencies learned in the exercises.
Seminar: Presentation (lasting 30–40 minutes) with written report (10-15 pages)
The students present, after coordinating the specific task with the examiner, the significance and systematic-scientific classification of a problem addressed in the seminar and explain and present their topic in writing in their report, incorporating aspects from the discussion in the seminar. The task may also include the elaboration of an application (i.e., programming/calculation, etc.) of a method to a typically practically significant individual case. The presentation with report refers to the content conveyed in the seminar and developed in the exercises.
Both portfolio elements will be assessed by an examiner. A final overall assessment will be provided.
Partial Examination 2: (practical Seminar 1 + practical Seminar 2)
Presentation (lasting 30–40 minutes) with written report (10-15 pages)
The students present, after coordinating the specific task with the examiner, the significance and systematic-scientific classification of a problem addressed in the seminar and explain and present their topic in writing in their report, incorporating aspects from the discussion in the seminar. The task may also include the elaboration of an application (i.e., programming/calculation, etc.) of a method to a typically practically significant individual case. The presentation with report refers to the content conveyed in the seminar.
OR
Partial Examination 2: (project course)
Project report (8-10 pages)
| Degree programme | Recommended start 3 | Duration | Mandatory option 4 |
|---|---|---|---|
| Intelligent Interactive Systems / Master of Science [FsB vom 16.05.2023 mit Änderungen vom 15.12.2023 und 01.04.2025 und Berichtigung vom 16.07.2024] | 2. o. 3. | one or two semesters | Compulsory optional subject |
The system can perform an automatic check for completeness for this module.