ACIT4030 Machine Learning for images and 3D data Course description

This course will present the state of the art in algorithms for machine learning on images and 3D data. After a brief introduction to 3D geometry, we will cover topics related to deep learning for 3D data. We will in particular study deep neural architectures for 3D data such as point clouds, images, and shape graphs.The course covers applications like classification, segmentation, shape retrieval and correspondence detection. Recent work on shape synthesis and joint embedding will also be discussed.

A background in programming, machine learning, and linear algebra is an advantage. Knowledge of computer graphics and image processing is preferable, but not strictly required.

No formal requirements over and above the admission requirements.

Upon successful completion of the course, the candidate:

Knowledge

• has knowledge of problems within graphics and imaging that are applicable to machine learning, such as classification, segmentation, correspondence detection, and shape retrieval.
• has a good understanding of problems related to shape synthesis.

Skills

• is able to apply state-of-the art machine learning algorithms to real-world problems related to imaging and 3D graphics.

Competence

• is aware of the state of the art in algorithms for machine learning on 3D data.
• has experience with real world problems within the course domain, with a focus on solutions using deep neural architectures.

• Introduction to geometric modelling
• Convolutional neural networks in 3D
• Deep learning for point clouds
• Convolutional neural networks on graphs
• 3D synthesis and analysis
• Joint embedding for images and 3D data

Teaching approach is a combination of traditional weekly lectures and practical work on a semester group project. Lectures will present influential research for relevant topics. The semester group project exposes the student to a chosen real-world problem relevant to the course topic.

Practical training

The student will be exposed to programming with repositories such as ShapeNet and will have created solutions for real-world problems related to deep learning for 3D data.

The following required coursework must be approved before the student can take the exam:

Two mandatory group assignments consisting of technical tasks, summarized in reports (about 10 pages each).

The final grade will be based on:

• Individual student presentation (20% of the final grade)
• One individually written evaluation of another student presentation (500-1000 words) (10% of the final grade)
• Individual final project report (between 12 500 and 17 500 words) (70% of the final grade)

All three exams must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All printed and written aids and a calculator that cannot be used to communicate with others.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Associate Professor Henrik Lieng