Prerequisite/Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

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.

Learning objectives

Knowledge

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

Skills

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.

Competences

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

Type of instruction

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.

Exam

Exams