263-0008-00L  Computational Intelligence Lab

SemesterSpring Semester 2017
LecturersT. Hofmann
Periodicityyearly recurring course
Language of instructionEnglish



Catalogue data

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

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersT. Hofmann
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationIn 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.
Written aidsTwo 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.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
263-0008-00 VComputational Intelligence Lab2 hrs
Fri10-12ML D 28 »
T. Hofmann
263-0008-00 UComputational Intelligence Lab2 hrs
Thu15-17CAB G 51 »
16-18CAB G 61 »
Fri15-17CAB G 61 »
13.04.15-17CAB G 61 »
T. Hofmann
263-0008-00 AComputational Intelligence Lab
No presence required.
1 hrsT. Hofmann

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Computer Science MasterInterfocus CoursesOInformation