Applied Machine Intelligence (Applied Machine Learning)
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
EI70310 is a semester module in English language at Master’s level which is offered in summer semester.
This module description is valid from SS 2020 to SS 2020.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||75 h||5 CP|
Content, Learning Outcome and Preconditions
- Computer engineering
- Communications engineering
- Multimedia technology and human machine interaction
Additionally, basic knowledge of Python (or the motivation to learn it) is recommended.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||3||Applied Machine Intelligence - Deep Learning for Multimedia||Keimel, C. Kissel, M.||
Thu, 15:00–16:30, Z995
Tue, 09:45–11:15, Z995
|VI||3||Applied Machine Intelligence - Practical Concepts of Machine Learning||
Assistants: Paukner, P.
Tue, 11:30–13:00, Z995
Learning and Teaching Methods
The written assignments (homework) and the project work enable the students to apply the learned concepts from the lecture to real-life applications and problems. During the project, the students will be supported by dedicated tutorials.
- Example code for algorithms
- Lecture notes
“Automatic Speech Recognition: A Deep Learning Approach,” D. Yu, L. Deng, Springer, London, 2015
“Design of Video Quality Metrics with Multi-Way Data Analysis: A data driven approach, “ C. Keimel, Springer Singapore, 2016
“Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems,” A. Géron, O’Reilly Media, 2017
Description of exams and course work
• Personal progress on theoretical and practical knowledge about information extraction from unstructured data shall be reflected on by each student individually and be documented in her/his personal Wiki page (Lab Book). Grading will be based on regularity, structure and relevance of the records.
• The students’ abilities to solve problems in the area of information extraction from unstructured data by applying machine learning and the thus necessary ability to apply and adapt the theoretical knowledge will be assessed in Milestone deliverables. The milestones are designed to guide the students through the project emphasizing a clear focus on important practical and theoretical tasks in the application of machine learning. For milestones, students produce software as well as project proposals and research questions. Grading is based on completeness and meeting the deadline.
• The ability of students to use the concepts of information extracting using machine learning in real-life applications taking into account constraints in realistic use-cases will be assessed by the results of the project and a corresponding presentation of these results. Moreover, the students’ general abilities of successful performing in a team including self-organisation will also be assessed by the successful completion of the project.
To pass the module, each of the above parts has to be passed. If each of the above parts is passed, the overall grade is the weighted sum of the above components:
- 40 % Personal Lab Book (individual)
- 20 % Project Milestones (group)
- 40 % Project and Final Report (group)
There is a possibility to take the exam in the following semester.