Data Science Master |
Core Courses |
Core Electives |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
263-4400-00L | Advanced Graph Algorithms and Optimization Number of participants limited to 30. | W | 5 credits | 3G + 1A | |
263-4400-00 G | Advanced Graph Algorithms and Optimization | | | 3 hrs | | R. Kyng |
263-4400-00 A | Advanced Graph Algorithms and Optimization | | | 1 hrs | | R. Kyng |
263-5300-00L | Guarantees for Machine Learning | W | 5 credits | 2V + 2A | |
263-5300-00 V | Guarantees for Machine Learning
Special selection process. Preference is given to Masters and Doctorate students. If need be other criteria are degree program and previous courses taken. | | | 2 hrs | | F. Yang |
263-5300-00 A | Guarantees for Machine Learning | | | 2 hrs | | F. Yang |
401-0674-00L | Numerical Methods for Partial Differential Equations Not meant for BSc/MSc students of mathematics. | W | 10 credits | 2G + 2U + 2P + 4A | |
401-0674-00 G | Numerical Methods for Partial Differential Equations
This course is designed in a flipped classroom format based on video tutorials and supplemented by a weekly question-and-answer session, for which attendance is highly recommended. | | | 2 hrs | | R. Hiptmair |
401-0674-00 U | Numerical Methods for Partial Differential Equations
Groups are selected in myStudies.
| | | 2 hrs | | R. Hiptmair |
401-0674-00 P | Numerical Methods for Partial Differential Equations
Homework C++ coding projects for the course "Numerical Methods for Partial Differential Equations" | | | 2 hrs | | R. Hiptmair |
401-0674-00 A | Numerical Methods for Partial Differential Equations
Video guided self-study or group-study for the course "Numerical Methods for Partial Differential Equations" | | | 4 hrs | | R. Hiptmair |
401-3052-05L | Graph Theory | W | 5 credits | 2V + 1U | |
401-3052-05 V | Graph Theory | | | 28s hrs | | B. Sudakov |
401-3052-05 U | Graph Theory | | | 7s hrs | | B. Sudakov |
401-3052-10L | Graph Theory | W | 10 credits | 4V + 1U | |
401-3052-10 V | Graph Theory | | | 4 hrs | | B. Sudakov |
401-3052-10 U | Graph Theory | | | 1 hrs | | B. Sudakov |
401-3602-00L | Applied Stochastic Processes | W | 8 credits | 3V + 1U | |
401-3602-00 V | Applied Stochastic Processes
Does not take place this semester. | | | 3 hrs | | not available |
401-3602-00 U | Applied Stochastic Processes
Does not take place this semester. | | | 1 hrs | | not available |
401-4632-15L | Causality | W | 4 credits | 2G | |
401-4632-15 G | Causality | | | 2 hrs | | C. Heinze-Deml |
401-4944-20L | Mathematics of Data Science | W | 8 credits | 4G | |
401-4944-20 G | Mathematics of Data Science
Planned to take place again in the Autumn Semester 2021. | | | 4 hrs | | A. Bandeira |
401-6102-00L | Multivariate Statistics | W | 4 credits | 2G | |
401-6102-00 G | Multivariate Statistics
Does not take place this semester. | | | 2 hrs | | not available |
402-0448-01L | Quantum Information Processing I: Concepts This theory part QIP I together with the experimental part 402-0448-02L QIP II (both offered in the Spring Semester) combine to the core course in experimental physics "Quantum Information Processing" (totally 10 ECTS credits). This applies to the Master's degree programme in Physics. | W | 5 credits | 2V + 1U | |
402-0448-01 V | Quantum Information Processing I: Concepts | | | 2 hrs | | P. Kammerlander |
402-0448-01 U | Quantum Information Processing I: Concepts | | | 1 hrs | | P. Kammerlander |
701-0104-00L | Statistical Modelling of Spatial Data | W | 3 credits | 2G | |
701-0104-00 G | Statistical Modelling of Spatial Data | | | 2 hrs | | A. J. Papritz |
|
Interdisciplinary Electives |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
101-0478-00L | Measurement and Modelling of Travel Behaviour | W | 6 credits | 4G | |
101-0478-00 G | Measurement and Modeling of Travel Behaviour | | | 4 hrs | | K. W. Axhausen |
103-0228-00L | Multimedia Cartography Prerequisite: Successful completion of Cartography III (103-0227-00L). | W | 4 credits | 3G | |
103-0228-00 G | Multimedia Cartography | | | 3 hrs | | H.‑R. Bär,
R. Sieber |
103-0247-00L | Mobile GIS and Location-Based Services | W | 5 credits | 4G | |
103-0247-00 G | Mobile GIS and Location-Based Services | | | 4 hrs | | P. Kiefer |
103-0255-01L | Geodata Analysis | W | 2 credits | 2G | |
103-0255-01 G | Geodatenanalyse | | | 2 hrs | | K. Kurzhals |
227-0945-10L | Cell and Molecular Biology for Engineers II This course is part II of a two-semester course. Knowledge of part I is required. | W | 3 credits | 2G | |
227-0945-10 G | Cell and Molecular Biology for Engineers II | | | 2 hrs | | C. Frei |
227-0391-00L | Medical Image Analysis Basic knowledge of computer vision would be helpful. | W | 3 credits | 2G | |
227-0391-00 G | Medical Image Analysis | | | 2 hrs | | E. Konukoglu,
M. A. Reyes Aguirre |
261-5113-00L | Computational Challenges in Medical Genomics Number of participants limited to 20. | W | 2 credits | 2S | |
261-5113-00 S | Computational Challenges in Medical Genomics | | | 2 hrs | | A. Kahles,
G. Rätsch |
261-5120-00L | Machine Learning for Health Care Number of participants limited to 150. | W | 5 credits | 3P + 1A | |
261-5120-00 P | Machine Learning for Health Care | | | 3 hrs | | G. Rätsch,
J. Vogt,
V. Boeva |
261-5120-00 A | Machine Learning for Health Care | | | 1 hrs | | G. Rätsch,
J. Vogt,
V. Boeva |
262-0200-00L | Bayesian Phylodynamics | W | 4 credits | 2G + 2A | |
262-0200-00 G | Bayesian Phylodynamics
***ATTENTION: Starting with the lecture on March18, the Bayesian Phylodynamics lecture will be broadcasted using a Zoom videoconference. The lecturer will inform the students about the URL to participate in the online course*** | | | 2 hrs | | T. Stadler,
T. Vaughan |
262-0200-00 A | Bayesian Phylodynamics | | | 2 hrs | | T. Stadler,
T. Vaughan |