Course Visualization

  • Instructor: Prof. Dr. Bernd Fröhlich and Dr. Patrick Riehmann
  • Teaching assistants
    • Dr. Patrick Riehmann
    • M.Sc. Joshua Reibert
  • Lecture schedule 
    • Thursday, 13:30-15:00
    • First lecture: May 7, 2020
    • Location: Videos in Moodle (online)
      • Use the enrolment key weimar_vis_2020
  • Lab class schedule
    • Tuesday, 17:00 - 18:30 (Master CS4DM + Master HCI)
                      18:30 - 20:00 (Bachelor Medieninformatik, Master Digital Engineering)
    • First lab class: May 19, 2020
    • All lab classes will take place online with BigBlueButton in Moodle
  • Target audience
    • B.Sc. Medieninformatik
    • M.Sc. Computer Science and Media
    • M.Sc. Computer Science for Digital Media
    • M.Sc. Human-Computer Interaction
    • M.Sc. Digital Engineering
    • M.Sc. MediaArchitecture
  • ECTS Credits
    • 4.5 Credits (MI, CSM, CS4DM, HCI, DE, MA)
    • 1.5 Credits Final Project Visualization
  • Grading:
    • The final course grade will be calculated from the following weighted components:
      • Written exam (50%)
      • Exercises (50%)
    • The exercise grade will be calculated from the following weighted components: 
      • Information Visualization lab classes (5 assignments): 70%
      • Scientific Visualization lab classes (2 assignments): 30%
      • Please note: An overall exercise grade of 50% is required for you to be able to take the exam
  • Office hours by appointment only

Course Description

The first part of this course presents fundamental and advanced information visualization techniques for multi-dimensional and hierarchical data, graphs, time-series data, cartographic and categorical data. During the second half, algorithms and models for the scientific visualization of volumetric and vector-based data as well as corresponding out-of-core and level-of-detail techniques for handling very large datasets are introduced.

Various approaches presented in lectures will be studied, in part practically through labs and assignments, and with case studies. Lab classes focus on implementing, testing and evaluating the visualization approaches presented during the lectures. This course will be taught in English.

News

  • Due to the C19 situation the whole course will be held online in Moodle.

Lecture Notes

The documents from last year serve as a basis and will be further developed. The video material and the lecture pdfs (Adobe reader works best) are only available for your personal use! By downloading the material you agree that you do not further distribute it. The pdf files are only available from within the university network or through vpn.

Grading

  • Grading Information
    • The following scheme lists the grades depending on the percentage of the achieved points in the lab class:
      • 1.0   >=95.0
      • 1.1   [93.5-95.0)
      • 1.2   [92.0-93.5)
      • 1.3   [90.5-92.0)
      • 1.4   [89.0-90.5)
      • 1.5   [87.5-89.0)
      • 1.6   [86.0-87.5)
      • 1.7   [84.5-86.0)
      • 1.8   [83.0-84.5)
      • 1.9   [81.5-83.0)
      • 2.0   [80.0-81.5)
      • 2.1   [78.5-80.0)
      • 2.2   [77.0-78.5)
      • 2.3   [75.5-77.0)
      • 2.4   [74.0-75.5)
      • 2.5   [72.5-74.0)
      • ...
      • 3.0   [65.0-66.5)
      • ...
      • 4.0   [50.0-51.5)
      • Less than 50% is insufficient for an admission to the exam.
  • Contributions to the final grade
    • lab class assignments: 50%
      • InfoVis: 70%
      • SciVis: 30%
    • exam: 50%
    • final project (optional for 6 ECTS)
      • the course grade and the grade for the final project will be combined based on the respective ECTS