Pattern Recognition

Uitgebreide vaknaam	Pattern Recognition
Leerdoelen
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	Practisch werk (PR), Schriftelijk tentamen (WE), Verslag (R) (The written exam is a digital exam.)
Vaksoort	master
Coördinator	M.A. Dhali, MSc.
Docent(en)	M.A. Dhali, MSc.
Verplichte literatuur	Titel Auteur ISBN Prijs Articles, lecture slides (provided during the course and on Nestor) Pattern Classification (2001, 2nd Edition) New York: Wiley R.O. Duda, P.E. Hart & D.G. Stork 0-471-05669-3
Entreevoorwaarden	Mandatory: No prior knowledge is assumed. Please note that the student is expected to have a relevant BSc degree. Strongly advised: Students need prior knowledge from the following topics on a bachelor/pre-masters level: Statistics, Signals and Systems, Imperative Programming, Introduction to Artificial Intelligence, Calculus for Artificial Intelligence, Algorithms and Data structures, Linear Algebra and Multivariable Calculus. Students need knowledge experiences of programming with Python and Matlab. Recommended: Knowledge and experience with different function libraries for machine learning, image and signal processing, statistics, and computer vision
Opmerkingen	External students should first apply for admission through the Board of Examiners form, after they have gotten permission from their own BoE.
Opgenomen in	Opleiding Jaar Periode Type MSc Artificial Intelligence  (B - Mandatory Course Units Robotics) - semester I b verplicht Robotics MSc Artificial Intelligence  (B - Mandatory Course Units Computational Intelligence) - semester I b verplicht CI MSc Artificial Intelligence  (C - Elective Course Units) - semester I b keuze