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.
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 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.)
see course description
Performance assessment information (valid until the course unit is held again)
The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examination
written 120 minutes
Additional information on mode of examination
In 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.
Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.