Pattern Recognition for CS

Faculteit Science and Engineering
Jaar 2021/22
Vakcode WMCS011-05
Vaknaam Pattern Recognition for CS
Niveau(s) master
Voertaal Engels
Periode semester I b

Uitgebreide vaknaam Pattern Recognition for CS
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, ANN, CNN, GAN, and clustering, 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)
Vaksoort master
Co├Ârdinator 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.
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 Some CS master courses — including Pattern Recognition — have limited enrollment:
- CS students can always enter the course, regardless of whether the course is mandatory for them or not.
- The number of enrolments for other non-CS students is limited. These students need to meet the course prerequisite requirements as mentioned on Ocasys. Priority is given to students for which the course is an official elective (see list below).
- An exception can be made for exchange students, if they have a CS background: please contact the FSE International Office. See here for more info about the enrollment procedure.
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
MSc Mathematics: Science, Business and Policy  (Science, Business and Policy: Statistics and Big Data) - semester I b keuzegroep
MSc Mathematics: Statistics and Big Data  (Statistics and Big Data: Guided Choice) - semester I b guided choice