| Module: | Visual Pattern Recognition |
| Module numbers: | 41.5044 [PVL 41.5045; Module 41.50440] |
| Language: | english |
| Study programme: | Dualer Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
MN Data Science 2016 - Katalog DS-I: Data Science - Informatik |
| Type of course: | V+P = Lecture+Practical |
| Weekly hours: | 2+2 |
| Credit Points: | 6 |
| Exam: | written exam (writing examination) |
| Registering for exam | explicitly and independent of booking |
| PVL (e.g. Practical): | graded (Software demonstration, documentation and presentation) |
| PVL percentage: | 50% |
| Frequency of offering: | each winter semester (not yet offered) |
| Required knowledge: | Basic knowledge of graphical data processing |
| Learning objectives: | - Students can conceptualise systems for applied pattern recognition problems, especially in the area of medical decision support.
- Students can conceptualise and implement feature extraction techniques to represent colour, shape, and texture properties of objects in images.
- Students can conceptualise and implement supervised classification algorithms (e.g., linear classification, Support Vector Machines, Bayes classification, deep learning, etc.)
- Students can perform the training phase in supervised classification tasks including data selection, acquisition, and labelling.
- Students can conceptualise and implement unsupervised pattern recognition algorithms, especially sequential, fuzzy, and hierarchical clustering schemes.
- Students can plan, perform, and interpret experiments for comparative evaluation of pattern recognition systems.
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| Content: | - Feature extraction, selection, and transformation (e.g., Fourier and wavelet transform, Principal Component Analysis, etc.)
- Supervised pattern recognition algorithms including linear classification, Support Vector Machines, and Bayes decision scheme.
- Unsupervised pattern recognition techniques including sequential, hierarchical, and fuzzy clustering.
- Adaptivity in pattern recognition methods including the concept of relevance feedback.
- Evaluation of pattern recognition systems
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| Literature: | - Theodoridis and Koutroumbas, Pattern Recognition, Elsevier, 2009, ISBN: 978-1-59749-272-0
- Grzegorzek, Sensor Data Understanding, Logos, 2017, ISBN: 978-3-8325-4633-5
- Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN: 978-0387-31073-2
- Goodfellow, Bengio, and Courville, Deep Learning, MIT Press, 2016, ISBN: 978-0262-03561-3
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| Lecture style / Teaching aids: | Lecture
Practical projects in small groups of 2 to 3 students
Materials: slides, papers, demonstrators |
| Responsibility: | Marcin Grzegorzek |
| Released: | WS 2018/2019 |
| Professional competencies: | - formal, algorithmic, mathematical competencies: high
- analytical, design and implementation competencies: medium
- technological competencies: medium
- capability for scientific work: high
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| Interdisciplinary competencies: | - project related competencies: medium
- interdisciplinary expertise: basic technical and natural scientific competence, basic economic competence
- social and self-competencies: ability to work in a team, analytical competence, judging competence, deciding competence
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