Foundations of data-oriented visual computing

ECTS Credits

10

Level of Study

Bachelor/Master

Full-time/Part-time

Full-time

Teaching semester

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 programme in Informatics.

Objectives and Content

Objectives:

The main objective of course INF250 is to provide students with the necessary knowledge and the related skills for exploiting data in modern computing problems (particularly, in visual computing), along with general competence in data-oriented visual computing. Students of INF250 are introduced to a board spectrum of mathematical and computational solutions for turning data into application-dependent values (better models, decisions, etc.). After the successful completion of course INF250, the students know which solutions exist, how they work, and are capable of applying them to data-intense real-world problems.

Content:

Course INF250 addresses a broad variety of topics in the context of data-oriented visual computing, including useful concepts from linear algebra, methods for changing the representation of data (change of basis, etc.), methods for the fitting of models to data, optimization basics, useful basics in numerical differentiation and integration, selected topics from statistics and machine learning, as well as an introduction to image processing and visualization.

Learning Outcomes

After the successful 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 about linear systems and how to solve them
• understands the concept of a basis of a vector space and knows how to change the basis
• knows about selected decomposition methods and how to exploit them in order to achieve a more meaningful representation of data
• knows about data modeling and how to fit a simple model to the data
• understands analytical solutions as well as iterative approximations
• understands selected basics of optimization
• knows basic methods of numerical differentiation and integration
• understands selected basics in statistics, useful for data-oriented visual computing
• knows about basics of machine learning, useful for data-oriented visual computing
• knows selected basics in image processing and visualization
• knows the basics of GPU programming

Skills

The student

• is able to change the basis of a given data representation
• can apply SVD or PCA in order to better exploit the intrinsic value of given data
• is able to fit a simple model to given data
• can program an iterative solution to solve certain basic data-oriented problems
• is able to conduct a basic optimization on a given data-oriented problem
• can realize a basic numerical solution to a given derivation/integration problem
• can exploit basic statistical concepts in order to turn given data into actionable values
• can do simple image processing and some basic visualization
• can realize a basic solution based on GPU programming

General competence

The student

• can judge the appropriateness of a given solution with respect to a given problem
• can realize a more advanced problem, based on the basic knowledge from INF250 plus additional expertise based on further research

Required Previous Knowledge

INF100 and INF101 (or comparable);

MAT101 or MAT111 or MAT105 (or comparable);

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

Recommended Previous Knowledge

Basic knowledge about linear algebra is very helpful, when starting with this course. Experiences with object-oriented programming, for ex., in Java, are very helpful, too.

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

Teaching is done in the form of lectures, group meetings, and exercises (programming assignments)

• Lectures (about 25 double-units throughout the semester),
• group meetings (about 8 double-units throughout the semester),
• exercises (about 8 assignments with a deadline every other week)

Compulsory Assignments and Attendance

The students must achieve at least 40% of all possible points at the exam as well as with the exercises/assignments

Forms of Assessment

The form of assessment is:

• 3 hours written exam at the end of the semester (40% of the overall grade)
• exercises and assignments (60% of the overall grade)

Examination Support Material

Non-programmable calculator, according to the faculty regulations.

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 June 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.

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 Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.