ACIT5930 Master's Thesis, Phase 3 Course description

The master's thesis is a specialized individual research project. Phase 3 is dedicated to final analysis and/or prototype development and writing the Master's thesis. Prototypes and/or other products that are developed as part of the project can also be part of the final thesis.

In addition, there will be a series of workshops on the academic writing and effective communication of the thesis project, building on the workshops in Phase 1 and 2. Students will develop an awareness of the conventions of academic writing and the writing process and use a range of analytical tools and methods to develop their writing and writing practices as part of writing their thesis.

Guidelines for master's theses at the Faculty can be found here: Retningslinjer for masteroppgaver ved Fakultet for teknologi, kunst og design - Student - minside (oslomet.no)

To be eligible to write a master's thesis, all courses from the first year of the program must be successfully completed.

A student who has completed this course should have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of this course the student:

• has specialized knowledge on the specific areas of their Master thesis
• has a deep understanding of scientific writing as a process of both constructing and communicating meaning.
• is familiar with the structures and conventions of methods and results chapters.

Skills

On successful completion of this course the student:

• can clearly define and limit problem areas
• can connect his/her own project to relevant research literature
• can plan and carry out limited research or development projects
• can identify types and scopes of results which are required to ensure the claims and conclusions are scientifically valid
• can reflect on the decisions made and their consequences for the project
• can effectively draft, revise and develop the written communication of their research

General competence

On successful completion of this course the student:

• can apply knowledge and skills in new areas and carry out advanced projects
• can analyse and deal critically with developed products or collected data
• can carry out comprehensive independent study
• can contribute to the innovation of their field
• can apply effective writing strategies to diverse academic writing situations, including the writing of academic research articles.

Successful completion of Phase 1 and Phase 2 forms the basis for Phase 3. The work is carried out under the guidance of the supervisor appointed at the start of Phase 1.

In addition to the project work, there will be a series of online, asynchronous classes during which students will be provided with a range of analytical tools and methods to help develop their writing skills. Students will also receive formative feedback on draft versions of their texts from the course instructor and their peers, with a focus on the final master's thesis and draft research article.

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

1. a draft text of the masters thesis or draft research paper
2. a peer review of another students draft text of the masters thesis
3. a Process Memo (reflection on the feedback received from the thesis supervisor(s))

The final assessment will be based on the following four deliveries:

• A written Master thesis (Length: 20,000-30,000 words, using one of the available document templates).
• A draft research paper (Length: 3,000-6,000 words)
• Individual oral presentation (30 minutes).
• Submission of an artefact (either physical or digital) as part of the thesis is optional. Any artefact that has been developed by the student as part of the research project must be approved by the supervisor, the Master Thesis Coordinator must be informed, and the artefact must be made available in such a way to be inspected by the examiners. In the case of a physical artefact, video and images may be used to document its properties, eliminating the need for a physical inspection.

The master's thesis is assessed on the basis of the following criteria:

• The originality and/or relevance of the issues or research questions to the field of study.
• Clarity in the development of issues or research questions being addressed.
• Documentation and use of relevant theory and research, as well as systematic use of sources.
• Clarity in the relationship between issues/research questions being addressed, the method choices/methodologies employed and the resulting discussions/conclusions.
• Ability to collect, systematize, interpret/deconstruct and present knowledge in a clear way.
• Reflection on ethical issues in the research process.
• Written presentation (clear table of contents, accurate literature references, bibliography and appendices).

Theses are written in Norwegian or English. The oral exam can be taken in Norwegian or English, regardless of which language the thesis was written in.

The written thesis must be awarded a grade of A-E (preliminary grade) in order for a student to take the oral exam. The final grade is set after the oral exam. The grade can be adjusted up or down by one grade based on the oral exam. All exams must be passed in order to pass the course.

Students can appeal against the grade set for the written part of the exam. If the grade is changed after an appeal against the grade, and the oral exam has already been held, the oral exam must be retaken.

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.

This course will present the state of the art in algorithms for machine learning for the 3D environment. We will cover topics related to deep learning for 3D data such as point clouds, multi-view images, and shape graphs. The course covers applications like classification, segmentation, shape retrieval and scene representation.

Grade scale A-F.

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 3D shape and image 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.