Deep learning
About the 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.
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.
Admission requirements
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 competence - 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
Prerequisites
Anbefalte forkunnskaper
FYS-2010 Image Analysis, FYS-2021 Machine Learning, FYS-3012 Pattern recognition
Credit reduction
If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:
- FYS-8033 Deep Learning 8 ects
Teaching methods
Lectures: 30 hours
Exercises: 30 hours
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.Schedule
The schedules are normally finalized and published well in advance of the start of the semester, often a few weeks beforehand. This gives students the opportunity to organize their studies and prepare for upcoming activities.
It is recommended to check the schedule regularly, as changes may occur.
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
Examination
| Oral exam | Duration: 40 Minutes |
Grade: A–E, fail F |
To take an examination, the student must have passed the following coursework requirements
| Short written assignment, implementation of fundational deep learning algorithms | Grade: Approved – not approved |
| Large practical project with impelmentation of state-of-the-art deep learning | Grade: Approved – not approved |
Everything you need to know about before, during, and after the exam; registration, absence, appeals, and diplomas: UiT Exams homepage
More info about the coursework requirements
Students are required to deliver two presentations of their fellow students over the course of the project period. The first presentation shall address the project idea and research question, while the second shall provide an account of the project's progress and development.
Fellow students are expected to assume the role of evaluators two times during the project period and to provide constructive and academically grounded feedback during the presentations.
In addition, students are required to submit a concise written report on the project. The work process will form part of the basis for the assessment of the coursework requirement. The project as a hole will also constitute part of the foundation for the oral examination.