263-0008-00L  Computational Intelligence Lab

SemesterFrühjahrssemester 2015
DozierendeT. Hofmann
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarOffice hour always on Mondays from 11-12 in room CAB H53



Katalogdaten

KurzbeschreibungThis 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.
LernzielStudents 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.

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeT. Hofmann
FormSessionsprüfung
PrüfungsspracheEnglisch
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusschriftlich 120 Minuten
Zusatzinformation zum PrüfungsmodusThe 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.
Hilfsmittel schriftlichTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.

Lernmaterialien

Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Lehrveranstaltungen

NummerTitelUmfangDozierende
263-0008-00 VComputational Intelligence Lab2 Std.
Fr10-12CAB G 61 »
T. Hofmann
263-0008-00 UComputational Intelligence Lab2 Std.
Do15-17CAB G 51 »
16-18CAB G 61 »
Fr15-17CAB G 61 »
T. Hofmann
263-0008-00 AComputational Intelligence Lab
No presence required.
1 Std.T. Hofmann

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

Keine zusätzlichen Belegungseinschränkungen vorhanden.

Angeboten in

StudiengangBereichTyp
Informatik MasterVertiefungsübergreifende FächerOInformation