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263-0008-00L 8 Credits MSC D-ITET , D-MATH , D-INFK
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Computational Intelligence Lab

Lecturers & Examiners: Prof. Dr. Gunnar Rätsch
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
VVZ CR 3.76

Last Updated: 2026-02-05 16:07:37

Abstract

This 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.

Objective

Students 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 three to four 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.)

Content

see course description

General Information

Language
English
Levels
MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Keine
70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function.The practical project is an integral part of the course. Participation is mandatory.Failing the project results in a failing grade for the overall examination of Computational Intelligence Lab.

Course Components

Type Title Time & Place Hours
lecture Computational Intelligence Lab
  • Fri 10:15-12:00 (ML D 28)
2 h weekly
exercise Computational Intelligence Lab
Exercise: Fri 16-18 Q&A: Thu 14-15
  • Thu 14:00-15:00 (ON LI NE)
  • Fri 16:00-18:00 (ON LI NE)
2 h weekly
independent project Computational Intelligence Lab
No presence required.
No time listed 3 h weekly

Offered In