spring 2023
FYS-3033 Deep learning - 10 ECTS

Application deadline

Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester. Exchange students and Fulbright students: 1 October for the spring semester and 15 April for the autumn semester.

Type of course

The course is available as a singular course, also to students enrolled at other universities in Norway, exchange students and Fullbright students. The course will only be taught if there are sufficiently many students. Please contact the student adviser as soon as possible if you are interested in following the course.

Admission requirements

Admission requirements are a Bachelor's degree in physics or similar education, including specialization in physics worth the equivalent of not less than 80 ECTS credits. Local admission, application code 9371 - singular course at Master's level.

Course overlap

Du vil få en reduksjon i antall studiepoeng (som oppgitt under), dersom du avlegger eksamen i dette emnet og har bestått følgende emne(r) fra før av:

FYS-8033 Deep Learning 8 stp

Course content

Deep Learning, a subfield of machine learning, has in recent years achieved state-of-the-art performance for tasks such as image classification, object detection and natural language processing. This course will study recent deep learning methodology such as e.g. convolutional neural networks, autoencoders and recurrent neural networks, will discuss recent advances in the field, and will provide the students with the required background to implement, train and debug these models. There will be a significant practical component, where students will gain hands-on experience. The course will in addition to deep learning algorithms contain elements of image processing, pattern recognition and statistics.

Recommended prerequisites

FYS-2010 Image Analysis, FYS-2021 Machine Learning, FYS-3012 Pattern recognition

Objectives of the course

Knowledge - The student is able to

  • describe advanced deep learning techniques
  • describe the development of deep learning
  • discuss recent developments in the field and develop an understanding for when deep learning might not be the optimal methodology
  • discuss advanced deep learning techniques for specialized settings

Skills - The student is able to

  • explain the general idea behind deep learning as well as specific algorithms that are being used
  • apply the learned material to new applications or problem settings
  • use deep learning methodology for research and industrial settings using software libraries such as e.g. Theano or TensorFlow
  • carry out an advanced deep learning project after specifications
  • make appropriate method and architecture choices for a given application or problem setting

General comtence - The student is able to

  • give an interpretation of recent developments and provide an intuition of the open questions in the field
  • give an account of the impact of deep learning in modern society and communicate this to non-experts
  • implement and apply deep learning methods to applications of her/his choosing

Language of instruction and examination

The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.

Teaching methods

Lectures: 30 hours

Exercises: 30 hours


Information to incoming exchange students

This course is open for inbound exchange students.

Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: INBOUND STUDENT MOBILITY: COURSE COORDINATORS AT THE FACULTIES | UiT


Schedule

Examination

Examination: Date: Weighting: Grade scale:
  
Off campus exam 26.04.2023 10:00 4/10 A–E, fail F
Oral exam 6/10 A–E, fail F

Coursework requirements:

To take an examination, the student must have passed the following coursework requirements:

Submitted project assignment Approved/ Not approved
UiT Exams homepage

Re-sit examination

There is no access to a re-sit examination in this course.
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FYS-3033
  • Tidligere år og semester for dette emnet