263-0008-00L  Computational Intelligence Lab

SemesterSpring Semester 2017
LecturersT. Hofmann
Periodicityyearly course
Language of instructionEnglish


AbstractThis 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.
ObjectiveStudents 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.)
Contentsee course description