Selected Topics in Machine Learning
Postgraduate course
- ECTS credits
- 10
- Teaching semesters
- Autumn, Spring
- Course code
- INF368
- Number of semesters
- 1
- Teaching language
- English or Norwegian
- Resources
- Schedule
Course description
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.
Learning Outcomes
On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
The student
- knows the main methods in the considered field/topic.
Skills
The student
- is able to apply the main methods in the considered field/topic in order to solve concrete problems.
Level of Study
master/ph.d.
Semester of Instruction
Irregular
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
None
Access to the Course
Access to the course requires admission to a master's programme at The Faculty of Science and Technology
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
Portfolio assessment. The portfolio cnsist of hand-ins and oral exam. Hand-ins and oral exam must be passed as they tests the course's learning outcome. The weighting is announced on mittuib at the start of the semester. The portfolio can due to oral exam not be regarded.
In semester with no teaching, the exam will be early in the semester. The results from portfolio will be included.
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Assessment Semester
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
Reading List
The reading list will be available within July 1st for the autumn semester and December 1st for the spring semester
Course Evaluation
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
Examination Support Material
None
Programme Committee
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course Coordinator
Course coordinator and administrative contact person can be found on Mitt UiB, or contact studieveileder@ii.uib.no
Course Administrator
The Faculty of Science and Technology represented by the Department of Informatics is the course administrator for the course and study programme.