TTT4185 - Machine Learning for Signal Processing


Examination arrangement

Examination arrangement: School exam
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam 100/100 4 hours D

Course content

Basic methods for statistical pattern recognition/machine learning. Deep neural networks, support vector machines, misture models, hidden Markov models. 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

Further on evaluation

If there is a re-sit examination in August, the examination form may be changed from written to oral.

Course materials

The main book is Bishop's Pattern Classification and Machine Learning

Credit reductions

Course code Reduction From To
SIE2090 7.5
More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Technological subjects
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Electronic Systems


Examination arrangement: School exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD School exam 100/100 D INSPERA
Room Building Number of candidates
Summer UTS School exam 100/100 D INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU