Level of Study
Objectives and Content
The course contains the basic framework for constructing efficient methods for solving unconstrained optimization problems. Topics include line search, trust regions and derivative-free methods for unconstrained optimization. For constrained optimization the Karush-Kuhn-Tucker theory and basic solution techniques are presented. The close connection to Machine Learning and stochastic gradient descent is discussed.
On completion of the course INF272 the candidate will have the following learning outcomes.
- can explain what a continuous optimization problem is and how it can be solved.
- can explain the mathematical theory behind the solution algorithms for continuous optimization problems..
- can analyze the effectivity of solution methods for continuous optimization problems.
- can discuss the connection to Machine Learning.
Required Previous Knowledge
At least 120 ECTS in computer science, preferably including some mathematics
Credit Reduction due to Course Overlap
I274: 10 ECTS
Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences
Teaching and learning methods
Lectures / 4 hours per week
Exercises / 2 hours per week
Compulsory Assignments and Attendance
Compulsory assignments are valid two semesters, the semester of the approval and the following semester.
Forms of Assessment
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.
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 mailto:email@example.com 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.
mailto:firstname.lastname@example.org Student adviser
T: 55 58 42 00