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Undergraduate course

Foundations of data-oriented visual computing

  • ECTS credits10
  • Teaching semesterSpring
  • Course codeINF250
  • Number of semesters1
  • LanguageEnglish
  • Resources

Semester of Instruction

Spring.

The course requires a moderate understanding of mathematics (basics of linear algebra) and good knowledge of programming. It should be taken in the second half of a 3-years Bachelor program in Informatics.

Objectives and Content

Business as well as research is increasingly influenced by data-driven discoveries and prediction. This course focuses on foundations for data-intense informatics projects, in particular projects involving visual computing. Those foundations include the necessary mathematical basis (more from a users┬┐ point of view) as well as programming foundations. Students of this course acquire the competence and skills that are required for realizing a data-intense project from start to end, i.e., from analyzing data to visualizing and communicating the results. The course provides students with a broad foundation of methods and techniques in data science and introduces the most important computational data analysis techniques with an emphasis on their conceptual basis as well as their practical utilization.

Learning Outcomes

Knowledge

* Students will exemplify an understanding of common data fitting and optimization techniques and their applications.

* Students will be able to carry out numerical integration and derivation methods.

* Students will know to apply basic statistical methods and basic machine learning.

Skills

* Students will demonstrate an understanding of linear systems and will be able to exploit them in data-oriented computation.

* Students will addapt in practice selected principles of image processing and visualization.

* Students will be able to design computer programs for data analysis and scientific computing.

* Students will practice programming that expoits special opportunities (like exploiting the graphics card).

General competencies

* Students will judge the appropriateness of applying different data analysis techniques.

* Students will organize and structure the problem solving approaches in a team environment.

Required Previous Knowledge

INF100 and INF101 (or a comparable education); MAT101 (or MAT111, or any other comparable course).

Recommended Previous Knowledge

The course requires basic knowledge of programming and mathematics from earlier University education. Students should have passed at least one basic course from mathematics (ideally with basic training in linear algebra), and at least two courses about programming.

Access to the Course

This course is open to all students of natural sciences, in particular informatics, who do have a minimum qualification in mathematics (basic in linear algebra) and a good command of programming from earlier University education, especially students from Bachelor programs in Informatics at UiB.

Students, who consider to later take the Master program in visualization at UiB Informatics (MAMN-INF/VI), are strongly recommended to take this course in the second half of their Bachelor studies.

Teaching Methods and Extent of Organized Teaching

The course is built upon lectures, programming tutorials and programming assignments, as well as exercises. On average, students will meet up for lectures, tutorials, and exercises for 6 hours per week.

Compulsory Assignments and Attendance

The exercises must be attended. The programming assignments will be evaluated and must be passed. An exam (about the content of the lectures) needs to be passed, as well. Compulsory assignments are valid two semesters, the semester of the approval and the following semester.

Forms of Assessment

At the end of the semester there is a written exam (three hours). If less than 20 students are taking the course, the exam can be oral.

The exam is a closed-book exam, where only approved calculators are allowed.

The overall evaluation of the course is then a combination of the grading of the programming assignments and the exam.

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.

Subject Overlap

No overlap

Contact

Contact Information

studieveileder@ii.uib.no

Exam information

  • Type of assessment: Written examination

    Date
    26.09.2017, 09:00
    Duration
    3 hours
    Withdrawal deadline
    12.09.2017
    Examination system
    Inspera
    Digital exam
    Location