Advanced Deep Learning for Image Analysis

After this course you will be able to:

• create a project plan and develop it to solve problems based on advanced deep neural networks with image data;
• have insight about novel (state-of-the-art) methods in Deep Learning used with image data and their relationship with prior work;
• create (design and train) advanced neural networks from scratch and pre-trained models, and finetune the neural networks;
• judge different network architectures designs (for example, adversarial networks, diffusion models, attention-based models, graph-based models, among others), their training regimes (supervised, semi-supervised, un-supervised and self-supervised learning), and in what contexts they are suitable;
• differentiate among supervised, semi-supervised, un-supervised and self-supervised learning regimes;
• create neural networks using standard libraries, for example, Pytorch;
• create (design and write) a scientific paper presenting the results of the research done.

The PhD-variant will also look at selected new research articles within deep learning.

2 hours of lectures, discussion, and learning activities as well as 2 hours of practical exercises per week.

The course will have mandatory assignments as well as a project. The project work is done in small groups during the semester along with the other activities, and guidance will be given during the practical exercises. As such, students must attend the lectures and the practical exercises to gain the full benefit of the teachings.

The project will be to prepare and delimit a problem, propose a solution, develop it, and present the results. (The problem and solution must be approved before developing the project.) The results must be presented to the peers during the semester and through a paper draft describing the project and its results.

The PhD-variant will have an extended syllabus compared to the main course and the project will be more extensive than for the main course.

The evaluation will be a group presentation and a final written report for the groups project, which counts 100% towards the final grade. The presentation and the report have an individual component where the students must demonstrate their contribution. Students will be given individual grades.

All mandatory assignments must be approved prior to the exam.

It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: IN5310

In this course, postponed exams are not offered for exam candidates who are ill before the exam or who become ill during the exam. A deferred submission deadline may be offered. Deadlines may be extended up to 7 days; the duration of the extension depends on the duration of the exam. The illness must be documented with a doctor's certificate dated no later than the ordinary submission date. You must submit the doctor's certificate to the Student Administration at IFI before the submission deadline for the home exam.

https://www.uio.no/english/studies/examinations/illness-postponed/index.html