Data Science Master |
Kernfächer |
Wählbare Kernfächer |
Nummer | Titel | Typ | ECTS | Umfang | Dozierende |
---|
263-4400-00L | Advanced Graph Algorithms and Optimization Number of participants limited to 30. | W | 5 KP | 3G + 1A | |
263-4400-00 G | Advanced Graph Algorithms and Optimization | | | 3 Std. | | R. Kyng |
263-4400-00 A | Advanced Graph Algorithms and Optimization | | | 1 Std. | | R. Kyng |
263-5300-00L | Guarantees for Machine Learning | W | 5 KP | 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 Std. | | F. Yang |
263-5300-00 A | Guarantees for Machine Learning | | | 2 Std. | | F. Yang |
401-0674-00L | Numerical Methods for Partial Differential Equations Nicht für Studierende BSc/MSc Mathematik | W | 10 KP | 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 Std. | | R. Hiptmair |
401-0674-00 U | Numerical Methods for Partial Differential Equations
Gruppeneinteilung erfolgt über myStudies.
| | | 2 Std. | | 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 Std. | | 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 Std. | | R. Hiptmair |
401-3052-05L | Graph Theory | W | 5 KP | 2V + 1U | |
401-3052-05 V | Graph Theory | | | 28s Std. | | B. Sudakov |
401-3052-05 U | Graph Theory | | | 7s Std. | | B. Sudakov |
401-3052-10L | Graph Theory | W | 10 KP | 4V + 1U | |
401-3052-10 V | Graph Theory | | | 4 Std. | | B. Sudakov |
401-3052-10 U | Graph Theory | | | 1 Std. | | B. Sudakov |
401-3602-00L | Applied Stochastic Processes | W | 8 KP | 3V + 1U | |
401-3602-00 V | Applied Stochastic Processes
Findet dieses Semester nicht statt. | | | 3 Std. | | keine Angaben |
401-3602-00 U | Applied Stochastic Processes
Findet dieses Semester nicht statt. | | | 1 Std. | | keine Angaben |
401-4632-15L | Causality | W | 4 KP | 2G | |
401-4632-15 G | Causality | | | 2 Std. | | C. Heinze-Deml |
401-4944-20L | Mathematics of Data Science | W | 8 KP | 4G | |
401-4944-20 G | Mathematics of Data Science
Planned to take place again in the Autumn Semester 2021. | | | 4 Std. | | A. Bandeira |
401-6102-00L | Multivariate Statistics | W | 4 KP | 2G | |
401-6102-00 G | Multivariate Statistics
Findet dieses Semester nicht statt. | | | 2 Std. | | keine Angaben |
402-0448-01L | Quantum Information Processing I: Concepts Dieser theoretisch ausgerichtete Teil QIP I bildet zusammen mit dem experimentell ausgerichteten Teil 402-0448-02L QIP II, die beide im Frühjahrssemester angeboten werden, im Master-Studiengang Physik das experimentelle Kernfach "Quantum Information Processing" mit total 10 ECTS-Kreditpunkten. | W | 5 KP | 2V + 1U | |
402-0448-01 V | Quantum Information Processing I: Concepts | | | 2 Std. | | P. Kammerlander |
402-0448-01 U | Quantum Information Processing I: Concepts | | | 1 Std. | | P. Kammerlander |
701-0104-00L | Statistical Modelling of Spatial Data | W | 3 KP | 2G | |
701-0104-00 G | Statistical Modelling of Spatial Data | | | 2 Std. | | A. J. Papritz |
|
Interdisziplinäre Wahlfächer |
Nummer | Titel | Typ | ECTS | Umfang | Dozierende |
---|
101-0478-00L | Measurement and Modelling of Travel Behaviour | W | 6 KP | 4G | |
101-0478-00 G | Measurement and Modeling of Travel Behaviour | | | 4 Std. | | K. W. Axhausen |
103-0228-00L | Multimedia Cartography Voraussetzung: Erfolgreicher Abschluss der Lerneinheit Cartography III (103-0227-00L). | W | 4 KP | 3G | |
103-0228-00 G | Multimedia Cartography | | | 3 Std. | | H.‑R. Bär,
R. Sieber |
103-0247-00L | Mobile GIS and Location-Based Services | W | 5 KP | 4G | |
103-0247-00 G | Mobile GIS and Location-Based Services | | | 4 Std. | | P. Kiefer |
103-0255-01L | Geodatenanalyse | W | 2 KP | 2G | |
103-0255-01 G | Geodatenanalyse | | | 2 Std. | | 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 KP | 2G | |
227-0945-10 G | Cell and Molecular Biology for Engineers II | | | 2 Std. | | C. Frei |
227-0391-00L | Medical Image Analysis Basic knowledge of computer vision would be helpful. | W | 3 KP | 2G | |
227-0391-00 G | Medical Image Analysis | | | 2 Std. | | E. Konukoglu,
M. A. Reyes Aguirre |
261-5113-00L | Computational Challenges in Medical Genomics Number of participants limited to 20. | W | 2 KP | 2S | |
261-5113-00 S | Computational Challenges in Medical Genomics | | | 2 Std. | | A. Kahles,
G. Rätsch |
261-5120-00L | Machine Learning for Health Care Number of participants limited to 150. | W | 5 KP | 3P + 1A | |
261-5120-00 P | Machine Learning for Health Care | | | 3 Std. | | G. Rätsch,
J. Vogt,
V. Boeva |
261-5120-00 A | Machine Learning for Health Care | | | 1 Std. | | G. Rätsch,
J. Vogt,
V. Boeva |
262-0200-00L | Bayesian Phylodynamics | W | 4 KP | 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 Std. | | T. Stadler,
T. Vaughan |
262-0200-00 A | Bayesian Phylodynamics | | | 2 Std. | | T. Stadler,
T. Vaughan |