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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Visualization

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Code Completion Credits Range Language
BE4M39VIZ Z,ZK 6 2P+2C
The course cannot be taken simultaneously with:
Visualization (B4M39VIZ)
The course is a substitute for:
Visualization (B4M39VIZ)
Lecturer:
Ladislav Čmolík (guarantor), Pavel Slavík
Tutor:
Ladislav Čmolík (guarantor)
Supervisor:
Department of Computer Graphics and Interaction
Synopsis:

In this course, you will get the knowledge of theoretical background

for visualization and the application of visualization in real-world

examples. The visualization methods are aimed at exploiting both the

full power of computer technologies and the characteristics (and

limits) of human perception. Well-chosen visualization methods can

help to reveal hidden dependencies in the data that are not evident at

the first glance. This in turn enables a more precise analysis of the

data, or provides a deeper insight into the core of the particular

problem represented by the data.

Requirements:
Syllabus of lectures:

1. Motivation for data visualization, history, categories of visualization 9infovis, scivis, software visualization,..)

2. Visualization of scalar data (visualization pipeline, data reduction)

3. Visualization of vector data (problems of visualization in 2D, 3D,..)

4. Visualization of volume data (marching cube, cuberille)

5. Visualization of volume data (volume data rendering, topological problems of volume data rendering,..)

6. Visualization of dynamic data (animation, time scale,..)

7. Information visualization (HomeFinder, TreeMaps, hyperbolic geometry)

8. Perception and interpretation of visualized data (context, human perception, psychology of perception)

9. Simulation and visualization (e.g. simulation and visualization of technological processes)

10. Visualization of medical data (tomography. Operation planning)

11. Technical illustration, medical illustration

12. Software visualization (visualization of software behavior, visualization of software maintenance ,..)

13. Problems of visual data mining. Applications of visual data mining (relation to neural computing)

14. Reserve

Syllabus of tutorials:

1. Semestral project assignement

2. Semestral project assignement

3. Consultations to semestral project

4. Consultations to semestral project

5. Consultations to semestral project

6. Consultations to semestral project

7. Checkpoint of semestral project

8. Consultations to semestral project

9. Consultations to semestral project

10. Consultations to semestral project

11. Consultations to semestral project

12. Semestral project presentation

13. Semestral project presentation

14. Semestral project assessment

Study Objective:
Study materials:

1. Fayyad, U., Grinstein, G.G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2002

2. Stasko,J., Domingue,J., Brown,M.H., Price, B.A.: Software Visualization, MIT Press, 1998

3. Chen, Ch.: Information Visualization and Virtual Environments,Springer, 1999

Note:
Further information:
https://moodle.fel.cvut.cz/course/B4M39VIZ
Time-table for winter semester 2019/2020:
Time-table is not available yet
Time-table for summer semester 2019/2020:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Fri
Thu
roomKN:E-301
Slavík P.
Čmolík L.

11:00–12:30
(lecture parallel1)
Karlovo nám.
Šrámkova posluchárna K9
roomKN:E-327
Čmolík L.
14:30–16:00
(lecture parallel1
parallel nr.101)

Karlovo nám.
Solarium K327
Fri
The course is a part of the following study plans:
Data valid to 2020-01-22
For updated information see http://bilakniha.cvut.cz/en/predmet4699706.html