course-details-portlet

TTT4185 - Machine Learning for Signal Processing

About

Examination arrangement

Examination arrangement: Home examination
Grade: Passed/Failed

Evaluation form Weighting Duration Examination aids Grade deviation
Home examination 100/100 4 hours

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

Further on evaluation

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

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Course materials

Text book will be announced at semester start.

Credit reductions

Course code Reduction From To
SIE2090 7.5
More on the course

No

Facts

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

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2020

No.of lecture hours: 4
Lab hours: 2
No.of specialization hours: 6

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

Phone:

Examination

Examination arrangement: Home examination

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Home examination 100/100

Release 2020-12-15

Submission 2020-12-15

Release 09:00

Submission 13:00

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.
Examination

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

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