263-0008-00L  Computational Intelligence Lab

SemesterFrühjahrssemester 2018
DozierendeT. Hofmann
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarOnly for master students, otherwise a special permission by the study administration of D-INFK is required.



Lehrveranstaltungen

NummerTitelUmfangDozierende
263-0008-00 VComputational Intelligence Lab2 Std.
Fr10:15-12:00ML D 28 »
T. Hofmann
263-0008-00 UComputational Intelligence Lab2 Std.
Do15:15-17:00CAB G 51 »
16:15-18:00CAB G 61 »
Fr15:15-17:00CAB G 61 »
T. Hofmann
263-0008-00 AComputational Intelligence Lab
No presence required.
1 Std.T. Hofmann

Katalogdaten

KurzbeschreibungThis laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications.
LernzielStudents acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems that solve real-world problems. They learn to successfully develop solutions to application problems by following the key steps of modeling, algorithm design, implementation and 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. Collaborative filtering and recommender systems, 2. Text sentiment classification, and 3. Road segmentation in aerial imagery.

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.

(Disclaimer: The offered projects may be subject to change from year to year.)
Inhaltsee course description

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte8 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 PrüfungsmodusIn case that the exam grade is >=3.5, the final grade will be determined by the written final exam (2/3 weight) and the maximum grade of the exam and the project (1/3 weight). If the exam grade is <3.5, the final grade will equal the exam grade.

Semester group effort: writing a short scientific paper that presents a novel solution to an application problem, and compares it to baselines developed during the course. Per the formula above, your semester project only accounts for a bonus, i.e. it will only be counted if it exceeds your exam grade.
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.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

Keine zusätzlichen Belegungseinschränkungen vorhanden.

Angeboten in

StudiengangBereichTyp
Data Science MasterWählbare KernfächerWInformation
Informatik MasterVertiefungsübergreifende FächerOInformation
Rechnergestützte Wissenschaften MasterWahlfächerWInformation