Pattern Recognition

Faculteit Science and Engineering
Jaar 2019/20
Vakcode INMPR-08
Vaknaam Pattern Recognition
Niveau(s) master
Voertaal Engels
Periode semester I b

Uitgebreide vaknaam Pattern Recognition
Leerdoelen Master various concepts and techniques for pattern recognition and get familiar with various applications.
Omschrijving This course provides an introduction to the theory and practice of pattern recognition. It is the research area that studies the design and operation of systems that detect, identify, recognise or classify patterns in data. Important application domains are image analysis (e.g. licence plate recognition or various medical applications), computer vision, speech analysis, man and machine diagnostics, person identification (e.g. by iris or fingerprint), spam filtering, industrial inspection, financial data analysis and forecast, genetics. Generally, pattern recognition includes techniques such as feature extraction, classification, and error estimation. The course presents various classification techniques, e.g. k-nn, LVQ, SVM, decision tree, and clustering techniques, e.g. k-means, VQ, dendrogram, gap statistics. Various applications are presented throughout the course.
Uren per week
Onderwijsvorm Hoorcollege (LC), Practisch werk (PRC)
Toetsvorm Schriftelijk tentamen (WE), Verslag (R)
(Your final mark will be computed as a weighted average of your marks for the practicals (50%) and the written examination (50%). In order to pass, you must get a sufficient (i.e. at least 6) for each of these separately. You will be admitted to the written (re-)examination only if you fulfill the requirements for the practicals. Successfully completing the practicals is a necessary condition for admission to the examination. 'Successfully' means getting a mark 'sufficient' or higher. The result weights for 50% in the final mark.)
Vaksoort master
Coördinator prof. dr. N. Petkov
Docent(en) prof. dr. N. Petkov
Verplichte literatuur
Titel Auteur ISBN Prijs
see list of recommended literature in Nestor
Entreevoorwaarden Students who have a bachelor degree in Computer Science from the UG will be admitted to the course. Students with other bachelor degrees or coming from other programmes or universities need prior knowledge from the following CS bachelor courses: Statistics, Signals and Systems, Imperative Programming, Calculus for Computing Science, Discrete Structures, Algorithms and Data structures, Linear Algebra and Multivariable Calculus, Advanced Algorithms and Data Structures, Introduction to Intelligent Systems, or equivalent courses taught elsewhere. Students need knowledge of and programming experience with MatLab and/or Python and/or Mathematica, C, C++. Students who do not present proof of the above credentials will not be admitted to the course. Knowledge of and experience with function libraries for machine learning, image and signal processing, statistics and computer vision is recommended.
Opmerkingen This course has LIMITED CAPACITY for students outside of the CS programme:
  • CS students always get unlimited access, as do students from other programmes for which this is a *compulsory* course (see list below). For other non-CS students who want to take this course as an elective, there is a limited number of places available, on a first-come first-served basis. An exception can be made for exchange students, if they have a CS background: please contact the FSE International Office.
  • Please make sure to enroll for the right item in ProgressWWW: *Group 1* (CS students and students from other programmes for which the course is compulsory): enrollments are unlimited. *Group 2* (non-CS students for which the course is an elective): enrollments are on a first-come first-served basis until the capacity limit is reached.
Opgenomen in
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MSc Computing Science: Data Science and Systems Complexity  (Compulsory course units) 1 semester I b verplicht
MSc Computing Science: Intelligent Systems and Visual Computing  (Compulsory course units) 1 semester I b verplicht
MSc Computing Science: Science Business and Policy  (Elective course units) 1 semester I b keuze
MSc Computing Science: Software Engineering and Distributed Systems  (Guided choice course units) - semester I b keuze
MSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester I b Computing Science
MSc Mathematics: Science, Business and Policy  (Science, Business and Policy: Statistics and Big Data) - semester I b keuzegroep
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