Data Science Master |
Core Courses |
Data Analysis |
Information and Learning |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
252-0535-00L | Machine Learning | W | 8 credits | 3V + 2U + 2A | |
252-0535-00 V | Machine Learning
Vorlesung: Donnerstag im ML D 28 mit Videoübertragung im ML E 12 Freitag im HG F 1 mit Videoübertragung im HG F 3 | | | 3 hrs | | J. M. Buhmann |
252-0535-00 U | Machine Learning | | | 2 hrs | | J. M. Buhmann |
252-0535-00 A | Machine Learning
Project Work, no fixed presence required. | | | 2 hrs | | J. M. Buhmann |
|
Statistics |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
401-3621-00L | Fundamentals of Mathematical Statistics | W | 10 credits | 4V + 1U | |
401-3621-00 V | Fundamentals of Mathematical Statistics | | | 4 hrs | | S. van de Geer |
401-3621-00 U | Fundamentals of Mathematical Statistics | | | 1 hrs | | S. van de Geer |
|
Data Management |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
263-3010-00L | Big Data | W | 8 credits | 3V + 2U + 2A | |
263-3010-00 V | Big Data | | | 3 hrs | | G. Fourny |
263-3010-00 U | Big Data | | | 2 hrs | | G. Fourny |
263-3010-00 A | Big Data
Individual work to get hands-on experience with the technologies covered, no fixed presence required. | | | 2 hrs | | G. Fourny |
|
Core Electives |
Number | Title | Type | ECTS | Hours | Lecturers |
---|
252-0417-00L | Randomized Algorithms and Probabilistic Methods | W | 8 credits | 3V + 2U + 2A | |
252-0417-00 V | Randomized Algorithms and Probabilistic Methods | | | 3 hrs | | A. Steger,
E. Welzl |
252-0417-00 U | Randomized Algorithms and Probabilistic Methods | | | 2 hrs | | A. Steger,
E. Welzl |
252-0417-00 A | Randomized Algorithms and Probabilistic Methods
Project Work, no fixed presence required. | | | 2 hrs | | A. Steger,
E. Welzl |
252-1407-00L | Algorithmic Game Theory | W | 7 credits | 3V + 2U + 1A | |
252-1407-00 V | Algorithmic Game Theory | | | 3 hrs | | P. Penna |
252-1407-00 U | Algorithmic Game Theory | | | 2 hrs | | P. Penna |
252-1407-00 A | Algorithmic Game Theory
Project Work, no fixed presence required. | | | 1 hrs | | P. Penna |
252-1414-00L | System Security | W | 5 credits | 2V + 2U | |
252-1414-00 V | System Security | | | 2 hrs | | S. Capkun,
A. Perrig |
252-1414-00 U | System Security | | | 2 hrs | | S. Capkun,
A. Perrig |
263-0006-00L | Algorithms Lab Only for master students, otherwise a special permission by the student administration of D-INFK is required. | W | 8 credits | 4P + 1A | |
263-0006-00 P | Algorithms Lab | | | 4 hrs | | A. Steger,
E. Welzl,
P. Widmayer |
263-0006-00 A | Algorithms Lab
Project Work, no fixed presence required. | | | 1 hrs | | A. Steger,
E. Welzl,
P. Widmayer |
263-0007-00L | Advanced Systems Lab Only for master students, otherwise a special permission by the student administration of D-INFK is required. | W | 8 credits | 4P + 1A | |
263-0007-00 P | Advanced Systems Lab | | | 4 hrs | | G. Alonso |
263-0007-00 A | Advanced Systems Lab
Project Work, no fixed presence required. | | | 1 hrs | | G. Alonso |
263-2400-00L | Reliable and Interpretable Artificial Intelligence | W | 4 credits | 2V + 1U | |
263-2400-00 V | Reliable and Interpretable Artificial Intelligence | | | 2 hrs | | M. Vechev |
263-2400-00 U | Reliable and Interpretable Artificial Intelligence | | | 1 hrs | | M. Vechev |
263-2800-00L | Design of Parallel and High-Performance Computing | W | 7 credits | 3V + 2U + 1A | |
263-2800-00 V | Design of Parallel and High-Performance Computing | | | 3 hrs | | T. Hoefler,
M. Püschel |
263-2800-00 U | Design of Parallel and High-Performance Computing | | | 2 hrs | | T. Hoefler,
M. Püschel |
263-2800-00 A | Design of Parallel and High-Performance Computing
Project Work, no fixed presence required. | | | 1 hrs | | T. Hoefler,
M. Püschel |
263-3210-00L | Deep Learning Number of participants limited to 300. | W | 4 credits | 2V + 1U | |
263-3210-00 V | Deep Learning | | | 2 hrs | | T. Hofmann |
263-3210-00 U | Deep Learning | | | 1 hrs | | T. Hofmann |
263-5210-00L | Probabilistic Artificial Intelligence | W | 4 credits | 2V + 1U | |
263-5210-00 V | Probabilistic Artificial Intelligence
Am 22. und 29. September findet die Vorlesung in HG E 7 statt!
On September 22 and 29., the lecture will take place in room HG E 7.
Ab dem 06. Oktober findet die Vorlesung im HG E 7 statt mit Videoübertragung im HG E 3. | | | 2 hrs | | A. Krause |
263-5210-00 U | Probabilistic Artificial Intelligence | | | 1 hrs | | A. Krause |
263-5902-00L | Computer Vision | W | 6 credits | 3V + 1U + 1A | |
263-5902-00 V | Computer Vision | | | 3 hrs | | L. Van Gool,
V. Ferrari,
A. Geiger |
263-5902-00 U | Computer Vision | | | 1 hrs | | L. Van Gool,
V. Ferrari,
A. Geiger |
263-5902-00 A | Computer Vision | | | 1 hrs | | L. Van Gool,
V. Ferrari,
A. Geiger |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | |
401-0625-01 V | Applied Analysis of Variance and Experimental Design | | | 2 hrs | | L. Meier |
401-0625-01 U | Applied Analysis of Variance and Experimental Design
Mon 15-17 might not work for all different programmes where this course is offered. On sufficient demand, other slots for the exercise sessions can be offered. | | | 1 hrs | | L. Meier |
401-3601-00L | Probability Theory At most one of the three course units (Bachelor Core Courses) 401-3461-00L Functional Analysis I 401-3531-00L Differential Geometry I 401-3601-00L Probability Theory can be recognised for the Master's degree in Mathematics or Applied Mathematics. | W | 10 credits | 4V + 1U | |
401-3601-00 V | Probability Theory | | | 4 hrs | | A.‑S. Sznitman |
401-3601-00 U | Probability Theory
Tue 13-14 or Tue 14-15 starting in the second week of the semester. | | | 1 hrs | | A.‑S. Sznitman |
401-3901-00L | Mathematical Optimization | W | 11 credits | 4V + 2U | |
401-3901-00 V | Mathematical Optimization | | | 4 hrs | | R. Weismantel |
401-3901-00 U | Mathematical Optimization | | | 2 hrs | | R. Weismantel |
401-4619-67L | Advanced Topics in Computational Statistics | W | 4 credits | 2V | |
401-4619-00 V | Advanced Topics in Computational Statistics | | | 2 hrs | | N. Meinshausen |
227-0101-00L | Discrete-Time and Statistical Signal Processing | W | 6 credits | 4G | |
227-0101-00 G | Discrete-Time and Statistical Signal Processing | | | 4 hrs | | H.‑A. Loeliger |
227-0417-00L | Information Theory I | W | 6 credits | 4G | |
227-0417-00 G | Information Theory I | | | 4 hrs | | A. Lapidoth |
227-0427-00L | Signal and Information Processing: Modeling, Filtering, Learning | W | 6 credits | 4G | |
227-0427-00 G | Signal and Information Processing: Modeling, Filtering, Learning | | | 4 hrs | | H.‑A. Loeliger |