de | en

Applied Machine Intelligence (Applied Machine Learning)

Module EI70310

This Module is offered by Chair of Data Processing (Prof. Diepold).

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.

Basic Information

EI70310 is a semester module in English language at Master’s level which is offered in summer semester.

This Module is included in the following catalogues within the study programs in physics.

  • Catalogue of non-physics elective courses
Total workloadContact hoursCredits (ECTS)
150 h 75 h 5 CP

Content, Learning Outcome and Preconditions

Content

no info

Learning Outcome

After the successful participation in the module, students know the methods, algorithms and underlying machine learning concepts for extracting information from audio, visual, and textual unstructured content. They understand the real-life constraints and resulting requirements for the design, implementation, and application of information extraction from unstructured data. Students are able to apply and modify existing information extraction algorithms, taking into account real-life requirements. They are able to evaluate information extraction algorithms and methodologies with respect to their suitability for specific applications or services.

Preconditions

The lecture assumes basic knowledge of general topics discussed at undergraduate level (BSc.) in one of the following areas:
- 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

TypeSWSTitleLecturer(s)DatesLinks
VI 3 Applied Machine Intelligence - Deep Learning for Multimedia Keimel, C. Kissel, M. Tue, 09:45–11:15, Z995
Thu, 15:00–16:30, Z995
eLearning
VI 3 Applied Machine Intelligence - Practical Concepts of Machine Learning Diepold, K. Kissel, M.
Assistants: Paukner, P.
Tue, 11:30–13:00, Z995
eLearning

Learning and Teaching Methods

The course consists of frontal teaching and discussions about current research questions using literature.

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.

Media

The following media will be used
- Example code for algorithms
- Slides
- Lecture notes
- Videos

Literature

“Deep learning,” I. Goodfellow, Y. Bengio, A. Courville, Y. Bengio, MIT press, Cambridge, 2016

“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

Module Exam

Description of exams and course work

The assessment of the students‘ participation in the lecture is split into three components:

• 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)

Exam Repetition

There is a possibility to take the exam in the following semester.

Top of page