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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. Thomas Hofmann, Prof. Dr. Valentina Boeva
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 16:38:19

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

The goal of this Lab is to enable students to connect their mathematical background in linear algebra, analysis, probability, and optimization with their basic knowledge in machine learning and their general skill set in Computer Science to gain a deeper understanding of models and tools of great practical impact. Students will acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems. The course includes project work. Students work in groups of three to four people to develop solutions to an application problem. The course offers three application problems to choose from. For each of the 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. The students will also have the possibility to suggest their own application problem to solve if it satisfies the complexity criteria and fits the subject of the course.

Content

see course description

Resources

Learning Materials (Links)

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 is calculated as the 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 the Computational Intelligence Lab.The course includes four optional graded homework assignments that have a bonus function.

Course Components

Type Title Time & Place Hours
lecture Computational Intelligence Lab
  • Fri 10:15-12:00 (ML D 28)
  • 17.05 Date 10:15-12:00 (HG E 7)
2 h weekly
exercise Computational Intelligence Lab
  • Thu 14:15-16:00 (CHN C 14)
  • Fri 16:15-18:00 (CAB G 11)
2 h weekly
independent project Computational Intelligence Lab
No presence required.
No time listed 3 h weekly

Offered In