Suchergebnis: Lerneinheiten im Frühjahrssemester 2020

Elektrotechnik und Informationstechnologie Master Information
Master-Studium (Studienreglement 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.
Vertiefungsfächer
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.
NummerTitelTypECTSUmfangDozierende
227-0120-00LCommunication Networks Information W6 KP4GL. Vanbever
227-0147-00LVLSI II: Design of Very Large Scale Integration Circuits Information W6 KP5GF. K. Gürkaynak, L. Benini
227-0418-00LAlgebra and Error Correcting Codes Information W6 KP4GH.‑A. Loeliger
227-0150-00LSystems-on-chip for Data Analytics and Machine Learning
Previously "Energy-Efficient Parallel Computing Systems for Data Analytics"
W6 KP4GL. Benini
227-0155-00LMachine Learning on Microcontrollers Belegung eingeschränkt - Details anzeigen
Registration in this class requires the permission of the instructors. Class size will be limited to 30.
Preference is given to students in the MSc EEIT.
W6 KP3G + 2AM. Magno, L. Benini
227-0384-00LUltrasound Fundamentals, Imaging, and Medical Applications
Course is offered for the last time in Spring Semester 2020.
W4 KP3GO. Göksel
227-0436-00LDigital Communication and Signal ProcessingW6 KP2V + 2UA. Wittneben
227-0478-00LAcoustics II Information W6 KP4GK. Heutschi
227-0558-00LPrinciples of Distributed Computing Information W7 KP2V + 2U + 2AR. Wattenhofer, M. Ghaffari
227-0560-00LDeep Learning for Autonomous Driving Information Belegung eingeschränkt - Details anzeigen
Registration in this class requires the permission of the instructors. Class size will be limited to 80 students.
Preference is given to EEIT, INF and RSC students.
W6 KP3V + 2PD. Dai, A. Liniger
227-0707-00LOptimization Methods for EngineersW3 KP2GP. Leuchtmann
227-0948-00LMagnetic Resonance Imaging in MedicineW4 KP3GS. Kozerke, M. Weiger Senften
227-1032-00LNeuromorphic Engineering II Information
Information für UZH Studierende:
Die Lerneinheit kann nur an der ETH belegt werden. Die Belegung des Moduls INI405 ist an der UZH nicht möglich.

Beachten Sie die Einschreibungstermine an der ETH für UZH Studierende: Link
W6 KP5GS.‑C. Liu, T. Delbrück, G. Indiveri
151-0566-00LRecursive Estimation Information W4 KP2V + 1UR. D'Andrea
252-0526-00LStatistical Learning Theory Information W7 KP3V + 2U + 1AJ. M. Buhmann, C. Cotrini Jimenez
252-0579-00L3D Vision Information W5 KP3G + 1AM. Pollefeys, V. Larsson
227-0973-00LTranslational Neuromodeling Information W8 KP3V + 2U + 1AK. Stephan
263-5904-00LDeep Learning for Computer Vision: Seminal Work Information Belegung eingeschränkt - Details anzeigen
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 KP2SM. R. Oswald, Z. Cui
252-3900-00LBig Data for Engineers Information
This course is not intended for Computer Science and Data Science MSc students!
W6 KP2V + 2U + 1AG. Fourny
263-5300-00LGuarantees for Machine Learning Information Belegung eingeschränkt - Details anzeigen W5 KP2V + 2AF. Yang
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