Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

263-0008-00L  Computational Intelligence Lab

SemesterSpring Semester 2015
LecturersT. Hofmann
Periodicityyearly recurring course
Language of instructionEnglish
CommentOffice hour always on Mondays from 11-12 in room CAB H53

Catalogue data

AbstractThis laboratory course teaches fundamental concepts in computational science and machine learning based on matrix factorization. This method provides a powerful framework of numerical linear algebra that encompasses many important techniques, such as dimension reduction, clustering, combinatorial optimization and sparse coding.
ObjectiveStudents acquire the fundamental theoretical concepts related to a class of problems that can be solved by matrix factorization. Furthermore, they successfully develop solutions to application problems by following the paradigm of modeling - algorithm development - implementation - experimental validation.

This lab course has a strong focus on practical assignments. Students work in groups of two to three people, to develop solutions to three application problems:
1. Compression: Exploiting image statistics to compress an image with minimal perceptual loss.
2. Collaborative filtering: predicting a user interest, based on his own and other peoples ratings. The "Netflix prize" is one such example.
3. Inpainting: Filling in lost parts of an image based on its surroundings.

For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersT. Hofmann
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationThe final grade is determined by:
a) Semester group effort: writing a short scientific paper that presents a novel solution to one of the three application problems, and compares it to baselines developed during the course.
b) Written exam: 120 minutes of pen-and-paper problems.
Your final grade will be:
1. Your final grade (1/3 semester project, 2/3 written exam), if your written exam grade is greater or equal to 3.5.
2. Your written exam grade if it is below 3.5.
Written aidsTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.


263-0008-00 VComputational Intelligence Lab2 hrs
Fri10-12CAB G 61 »
T. Hofmann
263-0008-00 UComputational Intelligence Lab2 hrs
Thu15-17CAB G 51 »
16-18CAB G 61 »
Fri15-17CAB G 61 »
T. Hofmann
263-0008-00 AComputational Intelligence Lab
No presence required.
1 hrsT. Hofmann


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Computer Science MasterInterfocus CoursesOInformation