Přístupnostní navigace
E-application
Search Search Close
Course detail
FIT-IKRAcad. year: 2013/2014
The tasks of classification and pattern recognition, basic schema of a classifier, data and evaluation of individual methods, statistical pattern recognition, Bayes learning, maximum likelihood method, GMM, EM algorithm, discriminative training, kernel methods, hybrid systems, how to merge classifiers, basics of AdaBoost, structural recognition, speech processing applications - speaker recognition, language identification, speech recognition, keyword spotting, image processing - 2D object recognition, face detection, OCR, and natural language processing - document classification, text analysis.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
The students will learn to work in a team. They will also improve their programming skills and their knowledge of development tools.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended literature
Classification of course in study plans
branch BIT , 0 year of study, summer semester, elective
Lecture
Teacher / Lecturer
Syllabus
Fundamentals seminar
Project