Přístupnostní navigace
E-application
Search Search Close
Course detail
FIT-SURAcad. year: 2023/2024
The tasks of classification and pattern recognition, basic schema of a classifier, data and evaluation of individual methods, statistical pattern recognition, feature extraction, multivariate Gaussian distribution,, maximum likelihood estimation, Gaussian Mixture Model (GMM), Expectation Maximization (EM) algorithm, linear classifiers, perceptron, Gaussian Linear Classifier, logistic regression, support vector machines (SVM), feed-forward neural networks, convolutional and recurrent neural networks, sequence classification, Hidden Markov Models (HMM). Applications of the methods of speech and image processing.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
To get points from the exam, you need to get min. 20 points, otherwise the exam is rated 0 points.
Aims
Study aids
Prerequisites and corequisites
Basic literature
Recommended literature
Classification of course in study plans
specialization NISY , 0 year of study, summer semester, electivespecialization NSPE , 0 year of study, summer semester, compulsoryspecialization NBIO , 0 year of study, summer semester, electivespecialization NSEN , 0 year of study, summer semester, electivespecialization NVIZ , 0 year of study, summer semester, electivespecialization NGRI , 0 year of study, summer semester, electivespecialization NADE , 0 year of study, summer semester, electivespecialization NISD , 0 year of study, summer semester, electivespecialization NMAT , 0 year of study, summer semester, electivespecialization NSEC , 0 year of study, summer semester, electivespecialization NISY up to 2020/21 , 0 year of study, summer semester, compulsoryspecialization NCPS , 0 year of study, summer semester, electivespecialization NHPC , 0 year of study, summer semester, electivespecialization NNET , 0 year of study, summer semester, electivespecialization NMAL , 0 year of study, summer semester, compulsoryspecialization NVER , 0 year of study, summer semester, electivespecialization NIDE , 0 year of study, summer semester, electivespecialization NEMB , 0 year of study, summer semester, electivespecialization NEMB up to 2021/22 , 0 year of study, summer semester, elective
Lecture
Teacher / Lecturer
Syllabus
Seminar
Lectures will be immediately followed by demonstration exercises (1h weekly) where examples on data and real code will be presented. Code and data of all demonstrations will be made available to the students.
Project