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.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.

Learning objectives

Knowledge

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

Skills

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.

Competences

Students who complete the module will obtain the following qualifications:

• Analyze machine learning to a problem in media technology, and reflect on a variety of possibilities to recommend a solution
• Apply machine learning methods to this problem
• Evaluate, discuss and generalize the results and reflect on their implications regarding the problem and the data

Type of instruction

Refer to the overview of instruction types listed in § 17.

Exam

Exams