ACIT4030 Machine Learning for 3D Computer Vision Course description

At least two subjects (20 ECTS) from the first semester must have been passed before the student can start work with MT1.

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.

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 of 15 minutes (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 6000 and 11,000 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 registering 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.

Assessment is pass or fail.

Two internal examiners. External examiner is used periodically.

Assistant professor Sigrun Pahr Maennling

This phase is the beginning of a longer research project. The content will be relative to the student's project.

The academic writing workshops will cover topics such as;

• Variations in academic style
• Audience, purpose and style
• The writing process
• Disciplinary identity
• Academic language
• Vocabulary, grammar, sentence, paragraph and text
• Coherence and cohesion
• Directness and formality
• Avoiding common errors: e.g. digression, lack of thesis statement, misunderstanding one’s audience
• Analysing, discussing and responding to academic texts
• Article structures, including IMRAD