Course content

Basic methods for statistical pattern recognition/machine learning. Deep neural networks, support vector machines, random forests, hidden Markov models, Gaussian processes. Design, training and evaluation of machine learning models. Extraction of feature vectors with applications to speech technology, medical signal processing and multimedia signal processing.

Learning outcome

Knowledge The candidate has - good understanding of the theoretical principles and practical aspects of using statistical pattern recognition/machine learning - good understanding of best practice with regards to the use of training, validation and test data - broad knowledge on the properties of speech, medical and multimedia signals - broad knowledge on feature extraction for wide variety of signals Skill: The candidate can - use and/or design software for use in train and evaluate models based on machine learning methods - evaluate the performance of machine learning systems General competence: The candidate can - the insights in the interplay between basis technology and development of machine learning systems - conduct teamwork and documentation

Learning methods and activities

Lectures, mandatory computer exercises.

Compulsory assignments

• Computer assignments

Recommended previous knowledge

TTT4120 Digital Signal Processing or equivalent.

Course materials

Text book will be announced at semester start.

Credit reductions