Search result: Course units in Spring Semester 2019

Electrical Engineering and Information Technology Master Information
Master Studies (Programme Regulations 2018)
Signal Processing and Machine Learning
The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Signal Processing and Machine Learning ", see Link.

The individual study plan is subject to the tutor's approval.
Core Courses
These core courses are particularly recommended for the field of "Signal Processing and Machine Learning".
You may choose core courses form other fields in agreement with your tutor.

A minimum of 24 credits must be obtained from core courses during the MSc EEIT.
Foundation Core Courses
NumberTitleTypeECTSHoursLecturers
252-0220-00LIntroduction to Machine Learning Information Restricted registration - show details
Previously called Learning and Intelligent Systems.
W8 credits4V + 2U + 1AA. Krause
Advanced Core Courses
NumberTitleTypeECTSHoursLecturers
227-0434-10LMathematics of Information Information W8 credits3V + 2U + 2AH. Bölcskei
Specialization Courses
These specialization courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor.

A minimum of 40 credits must be obtained from specialization courses during the MSc EEIT.
NumberTitleTypeECTSHoursLecturers
227-0120-00LCommunication Networks Information W6 credits4GL. Vanbever
227-0147-00LVLSI II: Design of Very Large Scale Integration Circuits Information W6 credits5GF. K. Gürkaynak, L. Benini
227-0418-00LAlgebra and Error Correcting Codes Information W6 credits4GH.‑A. Loeliger
227-0150-00LSystems-on-chip for Data Analytics and Machine Learning Information
Previously "Energy-Efficient Parallel Computing Systems for Data Analytics"
W6 credits4GL. Benini
227-0155-00LMachine Learning on Microcontrollers Restricted registration - show details
Registration in this class requires the permission of the instructors. Class size will be limited to 25.
Preference is given to students in the MSc EEIT.
W3 credits2GM. Magno, L. Benini
227-0384-00LUltrasound Fundamentals, Imaging, and Medical Applications Restricted registration - show details
Number of participants limited to 60.
W4 credits3GO. Göksel
227-0436-00LDigital Communication and Signal Processing Information W6 credits2V + 2UA. Wittneben
227-0478-00LAcoustics II Information W6 credits4GK. Heutschi
227-0558-00LPrinciples of Distributed Computing Information W6 credits2V + 2U + 1AR. Wattenhofer, M. Ghaffari
227-0707-00LOptimization Methods for EngineersW3 credits2GP. Leuchtmann
227-0948-00LMagnetic Resonance Imaging in MedicineW4 credits3GS. Kozerke, M. Weiger Senften
227-1032-00LNeuromorphic Engineering II Information
Information for UZH students:
Enrolment to this course unit only possible at ETH. No enrolment to module INI405 at UZH.

Please mind the ETH enrolment deadlines for UZH students: Link
W6 credits5GT. Delbrück, G. Indiveri, S.‑C. Liu
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
252-0526-00LStatistical Learning Theory Information W7 credits3V + 2U + 1AJ. M. Buhmann
252-0579-00L3D Vision Information W4 credits3GM. Pollefeys, V. Larsson
227-0973-00LTranslational Neuromodeling Information W8 credits3V + 2U + 1AK. Stephan
263-5904-00LDeep Learning for Computer Vision: Seminal Work Information Restricted registration - show details
Number of participants limited to 24.

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 credits2SZ. Cui
252-3900-00LBig Data for Engineers Information
This course is not intended for Computer Science and Data Science students!
W6 credits2V + 2U + 1AG. Fourny
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