Vår 2020

FYS-8033 Deep Learning - 10 stp

The course is administrated by

Institutt for fysikk og teknologi

Type of course

The course is available as a singular course.

Course overlap

FYS-3033 Deep learning 8 stp

Course contents

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 in detail recent advances in the field, and will provide the students with the required background and up-to-date knowledge to implement, train and debug these models. There will be a significant practical component, where students will gain hands-on experience on contemporary problems. The course will in addition to deep learning algorithms contain elements of image processing, pattern recognition and statistics.

Application deadline

Registration deadline for PhD students at UiT - The Arctic University of Norway: September 1st/February 1st Application deadline for external applicants: June 1st/December 1st

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years or 3+ 2 years (or equivalent) may be admitted. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee. Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English PhD students at UiT The Arctic University of Norway register for the course through StudentWeb . External applicants apply for admission through SøknadsWeb. Application code 9303.

All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

Objective of the course

Knowledge - The student is able to¿

describe fundamental 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 approaches 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 advanced applications or problem settings
use and further develop 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 in an individual manner
make independent choices about methodology and methods of analysis and performance assessment for a given application or problem setting

General expertise - 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 advanced 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 %. In comparison to students on the master¿s level course FYS-3033, PhD students taking FYS-8033 will be required to do one or more extra or alternative project assignments where they are given less specified tasks and more personal responsibility with respect to e.g. the choice of data, methodology, method of analysis and performance assessment. Such assignments may be related to their PhD project. All modules in the portfolio are assessed as a whole and one combined grade is given.

Assessment scale: Letter grades A-F.

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.

Coursework requirements Access to the final examination requires submission and approval of project assignments.