Objectives:
When designing and developing interactive media systems and technology, one is often faced with looking for interesting patterns and trends in data of several dimensions, what is called 'multivariatedata.' This course presents theoretical concepts and practical tools for analyzing multivariate data and designing pattern recognition methods for multimedia applications. Many of these methods are used in, e.g., automatic speech recognition, face detection, web page ranking, etc. The course includes the following topics: multivariate probability density functions, Bayesian estimation and detection, Gaussian model, parameter estimation, assessment of classifiers and estimators, data fitting, supervised and unsupervised learning, parametric and non-parametric learning, feature selection and reduction, and clustering.
Students who complete the course module will obtain the following qualifications:
Understand multivariate statistics and describe how to model multivariate data, e.g., using probabilistic and parametric descriptions
Understand Bayesian classification
Understand supervised and non-supervised learning methods, e.g., k-means clustering, principal component analysis, nearest neighbor
Understand features and the process of feature extraction from data
Students who complete the course module will obtain the following qualifications:
Choose, implement and apply pattern recognition tools to solve classification problems, e.g., footstep detection from accelerometers, recognition of single spoken digits
Apply knowledge to compare classification methods in terms of performance and complexity
Apply theory of multivariate statistics and analyse multimedia data, e.g., speech and music, images of faces, etc.
Students who complete the course module will obtain the following qualifications:
Analyse a problem in your field in the context of multivariate statistics and pattern recognition, and reflect on a variety of possibilities to recommend a solution
Analyse features for this problem
Implement and evaluate a classifier for this problem, and discuss and generalize the results
Refer to the overview of instruction types listed in the start of chapter 3. The types of instruction for this course are decided in accordance with the current Framework Provisions and directions are decided and given by the Study Board for Media Technology.
Name of exam | Multivariate Statistics and Pattern Recognition |
Type of exam | Written or oral exam
In accordance with the current Framework Provisions and directions
on examination from the
Study Board for Media Technology: Oral or written examination with internal censor. The assessment is performed in accordance with the 7-point scale. |
ECTS | 5 |
Permitted aids | With certain aids:
See semester description |
Assessment | 7-point grading scale |
Type of grading | Internal examination |
Criteria of assessment | The criteria of assessment are stated in the Examination Policies and Procedures |
Danish title | Multivariat statistik og mønstergenkendelse |
Module code | MSNMEDM1145 |
Module type | Course |
Duration | 1 semester |
Semester | Autumn
|
ECTS | 5 |
Language of instruction | English |
Location of the lecture | Campus Aalborg, Campus Copenhagen, Campus Esbjerg |
Responsible for the module |
Study Board | Study Board of Media Technology |
Department | Department of Architecture, Design and Media Technology |
Faculty | Technical Faculty of IT and Design |