REREG3000 Annual Report and Accounts. GAAP Course description

Opptak til studiet.

Associate Professor Henrik Lieng

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.

Forelesninger, gruppearbeid og selvstudier.

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.

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.

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 of artificial intelligence and machine learning techniques and technologies, and how they can be applied in intelligent user interfaces
• specialized knowledge practical application areas of intelligent user interfaces
• specialized knowledge about how intelligence and automation in user interfaces affects users

Skills

On successful completion of this course the student can:

• assess the need for and feasibility of applying artificial intelligence and machine learning to a given user interface problem
• identify suitable artificial intelligence or machine learning techniques and technologies for a given user interface problem
• build rapid prototypes of intelligent user interfaces
• evaluate intelligent user interfaces

General competence

On successful completion of this course the student can:

• analyse ethical aspects of automatic collection, storage, automatic interpretation and use of person-related measurements
• analyse opportunities and limitations associated with artificial intelligence and machine learning for given problems.