Level of Study
Objectives and Content
The course deals with current topics in machine learning, and its content will vary from time to time the course is taught.
Topic for autumn semester 2022: Advanced deep learning: metric learning and latent representations
On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- knows the main methods in the considered field/topic.
- is able to apply the main methods in the considered field/topic in order to solve concrete problems.
Required Previous Knowledge
At least 120 ECTS in computer science, preferably including some mathematics
Recommended Previous Knowledge
INF264/INF283 or equivalent knowledge.
Credit Reduction due to Course Overlap
Access to the Course
Access to the course requires admission to a master's programme at The Faculty of Mathematics and Natural Sciences
Teaching and learning methods
Varies. The course may contain lectures, exercises and projects.
Compulsory Assignments and Attendance
Compulsory assignments are valid for one subsequent semesters.
Forms of Assessment
Oral exam. The compulsory exercises can be graded and this grade can count for the final grade. Both the exam and the compulsory exercises must be passed.
Examination Support Material
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.
T: 55 58 42 00
Type of assessment: Oral examination
- Exam period
- Withdrawal deadline