Objectives:
When designing and developing interactive media systems and technology, one is often faced with looking for interesting patterns and trends.This course presents theoretical concepts and practical tools for analyzing data for multimedia applications and solving machine learning problems, such as classification, in media techology. Many of these methods are used in, e.g., automatic speech recognition, face detection, web page ranking, autonomous driving, etc. The course includes the following topics: multivariate probability density functions, Bayesian classification, estimation, and detection, parametric (e.g., Gaussian density-based) and non-parametric classifiers (e.g. k-nn, parzen, convolutional neural networks), regression, data fitting, evaluation of classifiers and estimators, unsupervised and supervised learning (e.g., reinforcement learning), feature selection and reduction.
Students who complete the module will obtain the following qualifications:
Understand multivariate statistics and describe how to model multivariate data, e.g., using probabilistic and parametric descriptions
Understand the principles of Bayesian classification
Understand supervised (classification, regression) and unsupervised learning methods, (e.g., k-means clustering, principal component analysis)
Understand features, feature selection, and dimensionality reduction
Students who complete the 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 analyze multimedia data, e.g., speech and music, images of faces, etc.
Students who complete the module will obtain the following qualifications:
Refer to the overview of instruction types listed in § 17.
Name of exam | Machine Learning for Media Technology |
Type of exam | Oral exam based on a project |
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 | Machine learning i medieteknologi |
Module code | MSNMEDM1205 |
Module type | Course |
Duration | 1 semester |
Semester | Autumn
|
ECTS | 5 |
Language of instruction | English |
Location of the lecture | Campus Aalborg, Campus Copenhagen |
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 |