Search result: Courses in Spring Semester 2020

Data Science Master Information
Interdisciplinary Electives
NumberTitleTypeECTSHoursLecturers
851-0739-01LSequencing Legal DNA: NLP for Law and Political Economy
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
W3 credits2V
851-0739-01 VSequencing Legal DNA: NLP for Law and Political Economy2 hrs
Mon13:15-15:00LFW C 5 »
E. Ash
851-0739-02LSequencing Legal DNA: NLP for Law and Political Economy (Course Project)
This is the optional course project for "Building a Robot Judge: Data Science for the Law."

Please register only if attending the lecture course or with consent of the instructor.

Some programming experience in Python is required, and some experience with text mining is highly recommended.
W2 credits2V
851-0739-02 VSequencing Legal DNA: NLP for Law and Political Economy (Course Project)
Mondays, 1pm-3pm
28s hrsE. Ash
851-0740-00LBig Data, Law, and Policy Restricted registration - show details
Number of participants limited to 35

Students will be informed by 1.3.2020 at the latest.
W3 credits2S
851-0740-00 SBig Data, Law, and Policy
Permission from lecturers required for all students.
2 hrs
Wed13:15-15:00IFW E 42 »
19.02.13:15-15:00IFW A 36 »
S. Bechtold
363-1100-00LRisk Case Study Challenge Restricted registration - show details W3 credits2S
363-1100-00 SRisk Case Study Challenge Special students and auditors need a special permission from the lecturers.
Does not take place this semester.
2 hrsA. Bommier, S. Feuerriegel
Seminar
NumberTitleTypeECTSHoursLecturers
261-5113-00LComputational Challenges in Medical Genomics Information Restricted registration - show details
Number of participants limited to 20.
W2 credits2S
261-5113-00 SComputational Challenges in Medical Genomics2 hrs
Mon13:15-15:00CAB G 57 »
A. Kahles, G. Rätsch
263-3840-00LHardware Architectures for Machine Learning Information
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 credits2S
263-3840-00 SHardware Architectures for Machine Learning2 hrs
Thu15:15-17:00LEE C 104 »
G. Alonso, T. Hoefler, C. Zhang
263-5225-00LAdvanced Topics in Machine Learning and Data Science Restricted registration - show details
Number of participants limited to 20.

The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 credits2S
263-5225-00 SAdvanced Topics in Machine Learning and Data Science2 hrs
Wed16:15-18:00LFW E 13 »
F. Perez Cruz
401-3620-20LStudent Seminar in Statistics: Inference in Non-Classical Regression Models Restricted registration - show details
Number of participants limited to 24.
Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. Also offered in the Master Programmes Statistics resp. Data Science.
W4 credits2S
401-3620-00 SStudent Seminar in Statistics: Inference in Non-Classical Regression Models2 hrs
Mon15:15-17:00HG E 33.1 »
F. Balabdaoui
GESS Science in Perspective
NumberTitleTypeECTSHoursLecturers
851-0740-00LBig Data, Law, and Policy Restricted registration - show details
Number of participants limited to 35

Students will be informed by 1.3.2020 at the latest.
W3 credits2S
851-0740-00 SBig Data, Law, and Policy
Permission from lecturers required for all students.
2 hrs
Wed13:15-15:00IFW E 42 »
19.02.13:15-15:00IFW A 36 »
S. Bechtold
» see Science in Perspective: Type A: Enhancement of Reflection Capability
» Recommended Science in Perspective (Type B) for D-INFK
» see Science in Perspective: Language Courses ETH/UZH
Master's Thesis
NumberTitleTypeECTSHoursLecturers
261-0800-00LMaster's Thesis
The minimal prerequisites for the Master’s thesis registration are:
- Completed Bachelor’s program
- All additional requirements completed (additional requirements, if any, are listed in the admission decree)
- Minimum degree requirements fulfilled of the course categories Data Analysis and Data Management and overall 50 credits obtained in the course category Core Courses
- Data Science Lab (14 credits) completed
O30 credits64D
261-0800-00 DMaster's Thesis900s hrsProfessors
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