APDMB041 Intelligent Data Processing

Institute of physics in Opava
summer 2024
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Petr Čermák, Ph.D. (lecturer)
doc. Ing. Petr Čermák, Ph.D. (seminar tutor)
Guaranteed by
doc. Ing. Petr Čermák, Ph.D.
Institute of physics in Opava
Timetable
Wed 8:55–10:30 PU-UF
  • Timetable of Seminar Groups:
APDMB041/A: Wed 10:35–12:10 PU-UF, P. Čermák
Prerequisites
(FAKULTA(FU) && TYP_STUDIA(B))
None
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Students will get acquainted with selected methods of intelligent data processing, including decision-making over these data.
Learning outcomes
Upon completion of the course, the student will be able to: describe and explain sound processing and sound processing methods with a focus on speech processing;
describe and explain Markov's hidden chains, applications in voice recognition;
describe the basic chain of image processing;
describe the methods of segmentation, detection of segments and their classification;
describe and explain the detection of key points and uses;
describe the PCA dimension reduction method; describe and explain decision-making methods and expert systems.
Syllabus
  • 1. Audio processing, audio and speach recognition
  • 2. Markov hidden chains, application in voice recognition
  • 3. Image recognition, basic processing pipeline
  • 4. Selected methods of segmentation
  • 5. Analysis, detection and classification of image segments
  • 6. Detection of key points in the image
  • 7. Examples of segmentation, classification and detection of significant points in medical images (CT, MR, US)
  • 8. Multidimensional data processing, PCA, dimension reduction
  • 9. Multi-criteria decision-making over multidimensional data
  • 10. Expert systems
  • 11. Examples of data analysis for environmental monitoring.
Literature
    required literature
  • ZELINKA, I. Evoluční výpočetní techniky, principy a aplikace. Praha, 2008. ISBN 978-80-7300-218-3.
  • SZELISKI, Richard. Computer Vision: Algorithms and Applications. Berlin, 2010.
  • MAŘÍK a kol. Umělá inteligence I, II. Praha, 2001.
  • NOUZA, J.: Pokročilé metody rozpoznávání řeči. 2016 [cit 2018-01-13]. Dostupné online http://itakura.ite.tul.cz/jan/PMR/.
  • DOUGHERTY, G. Digital Image Processing for Medical Applications. Oxford, 2009. ISBN 978-0521860857.
  • LEVER, J., KRZYWINSKI, M., ALTMAN, N., Points of Significance . Principal component analysis, https://doi.org/10.1038/nmeth.4346.
Teaching methods
Forms of teaching will be as follows:
1. theoretical preparation (lectures);
2. laboratory exercises (processing of data).
Assessment methods
Active participation in seminars,demonstrating knowledge of the issue of the study subject on the oral examination.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms summer 2021, summer 2022, summer 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.slu.cz/course/fu/summer2024/APDMB041