spring 2021
FYS-3033 Deep learning - 10 ECTS

Last changed 11.08.2021

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

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

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


Assessment

Portfolio assessment of project assignments counting about 40 % and a final oral examination counting about 60 %. All modules in the portfolio are assessed as a whole and one combined grade is given.

Assessment scale: Letter grades A-F. F - fail.

Re-sit examination (section 22): There is no access to a re-sit examination in this course.

Postponed examination (sections 17 and 21): Students with valid grounds for absence will be offered a postponed examination. Both postponed laboratory exercises and postponed oral examination are arranged during the semester if possible, otherwise early in the following semester. See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.


Schedule

Recommended reading/syllabus

Pensumliste for FYS-3033 Deep Learning
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FYS-3033