Course of Study Computational Science and Engineering (Study Cohort w15)

Sample course plan R  Master Computational Science and Engineering (IIWMS)
Specialisation Systems Engineering
Legend:
Core qualification CompulsorySpecialisation CompulsoryFocus CompulsoryThesis Compulsory
Core qualification Elective CompulsorySpecialisation Elective CompulsoryFocus Elective CompulsoryInterdisciplinary complement
LP
Semester 1FormHrs/wk
Semester 2FormHrs/wk
Semester 3FormHrs/wk
Semester 4FormHrs/wk
1
Algebraic Statistics for Computational Biology
Algebraic Statistics for Computational BiologyVL2
Algebraic Statistics for Computational BiologyUE2
Nonlinear Optimization
Nonlinear OptimizationVL3
Nonlinear OptimizationUE1
Research Project and Seminar
SeminarSE2
Master Thesis
2
3
4
5
6
7
Robotics
Robotics: Modelling and ControlVL3
Robotics: Modelling and ControlUE2
Pattern Recognition and Data Compression
Pattern Recognition and Data CompressionVL4
8
9
10
11
12
13
Digital Image Analysis
Digital Image AnalysisVL4
Machine Learning and Data Mining
Machine Learning and Data MiningVL2
Machine Learning and Data MiningUE2
14
15
16
17
18
19
Intelligent Autonomous Agents and Cognitive Robotics
Intelligent Autonomous Agents and Cognitive RoboticsVL2
Intelligent Autonomous Agents and Cognitive RoboticsUE2
Robotics and Navigation in Medicine
Robotics and Navigation in MedicineVL2
Robotics and Navigation in MedicineUE1
Robotics and Navigation in MedicinePS2
3D Computer Vision
3D Computer VisionVL2
3D Computer VisionUE2
20
21
22
23
24
25
Mathematical Image Processing
Mathematical Image ProcessingVL3
Mathematical Image ProcessingUE1
26
27
28
29
30
Business & Management (from catalogue) - 6LP
Nontechnical Elective Complementary Courses for Master (from catalogue) - 6LP

The choice of courses from the catalogue is flexible (depends on the semestral work load), provided the necessary number of required credits is reached.