Suchergebnis: Lehrveranstaltungen im Herbstsemester 2020

Elektrotechnik und Informationstechnologie Master Information
Master-Studium (Studienreglement 2018)
Signal Processing and Machine Learning
The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html.

The individual study plan is subject to the tutor's approval.
Kernfächer
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
Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area.
NummerTitelTypECTSUmfangDozierende
227-0101-00LDiscrete-Time and Statistical Signal Processing Information W6 KP4G
227-0101-00 GDiscrete-Time and Statistical Signal Processing
The lecturers will communicate the exact lesson times of ONLINE courses.
4 Std.
Di14:00-18:00ON LI NE »
H.‑A. Loeliger
227-0105-00LIntroduction to Estimation and Machine Learning Belegung eingeschränkt - Details anzeigen W6 KP4G
227-0105-00 GIntroduction to Estimation and Machine Learning Für Fachstudierende und Hörer/-innen ist eine Spezialbewilligung der Dozierenden notwendig.
The lecturers will communicate the exact lesson times of ONLINE courses.
4 Std.
Fr14:00-18:00ON LI NE »
H.‑A. Loeliger
Advanced Core Courses
Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.
NummerTitelTypECTSUmfangDozierende
227-0423-00LNeural Network Theory Information W4 KP2V + 1U
227-0423-00 VNeural Network Theory
«Hybrid».
Up to 150 students can attend the course on-site. Further information will be announced to enrolled students by e-mail in the week before the semester starts.

The first lecture is on 21.9.
2 Std.
Mo10:15-12:00ETF C 1 »
H. Bölcskei
227-0423-00 UNeural Network Theory
«Hybrid».
Up to 150 students can attend the course on-site. Further information will be announced to enrolled students by e-mail in the week before the semester starts.

The first lecture is on 21.9.
1 Std.
Mo12:15-13:00ETF C 1 »
H. Bölcskei
227-0427-00LSignal Analysis, Models, and Machine Learning
This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester).
W6 KP4G
227-0427-00 GSignal Analysis, Models, and Machine Learning
Findet dieses Semester nicht statt.
This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester)
4 Std.H.‑A. Loeliger
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1U
227-0447-00 VImage Analysis and Computer Vision
The lecturers will communicate the exact lesson times of ONLINE courses.
3 Std.
Do14:00-17:00ON LI NE »
L. Van Gool, E. Konukoglu, F. Yu
227-0447-00 UImage Analysis and Computer Vision
The lecturers will communicate the exact lesson times of ONLINE courses.
1 Std.
Do17:00-18:00ON LI NE »
L. Van Gool, E. Konukoglu
252-0535-00LAdvanced Machine Learning Information W10 KP3V + 2U + 4A
252-0535-00 VAdvanced Machine Learning
The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality.
3 Std.
Do15:15-16:00ETA F 5 »
Fr08:15-10:00ETA F 5 »
J. M. Buhmann, C. Cotrini Jimenez
252-0535-00 UAdvanced Machine Learning2 Std.
Mi14:15-16:00CAB G 61 »
16:15-18:00CAB G 61 »
Do16:15-18:00ML F 34 »
Fr14:15-16:00CAB G 61 »
J. M. Buhmann, C. Cotrini Jimenez
252-0535-00 AAdvanced Machine Learning
Project Work, no fixed presence required.
4 Std.J. M. Buhmann, C. Cotrini Jimenez
263-3210-00LDeep Learning Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2A
263-3210-00 VDeep Learning
The lecturers will communicate the exact lesson times from «online» courses.
3 Std.
Mi13:00-14:00ON LI NE »
Do14:00-16:00ON LI NE »
T. Hofmann
263-3210-00 UDeep Learning2 Std.
Mo16:15-18:00NO C 60 »
Mi16:15-18:00ML F 36 »
T. Hofmann
263-3210-00 ADeep Learning2 Std.T. Hofmann
Vertiefungsfächer
These specialisation 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 specialisation courses during the MSc EEIT.
NummerTitelTypECTSUmfangDozierende
227-0116-00LVLSI I: From Architectures to VLSI Circuits and FPGAs Information W6 KP5G
227-0116-00 GVLSI I: From Architectures to VLSI Circuits and FPGAs
Lecture: Tue 8 - 10 h
Execises: Wed 13 - 16 h or Thu 09 - 12 h
The lecturers will communicate the exact lesson times of ONLINE courses.
5 Std.
Di08:00-10:00ON LI NE »
Mi13:15-16:00ETZ D 61.1 »
13:15-16:00ETZ D 61.2 »
13:15-16:00ETZ D 96.1 »
Do09:15-12:00ETZ D 61.1 »
09:15-12:00ETZ D 61.2 »
09:15-12:00ETZ D 96.1 »
F. K. Gürkaynak, L. 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 16.
Preference is given to students in the MSc EEIT.
W6 KP3G
227-0155-00 GMachine Learning on Microcontrollers Für Fachstudierende und Hörer/-innen ist eine Spezialbewilligung der Dozierenden notwendig.
Bewilligung der Dozierenden für alle Studierenden notwendig.
All the lectures will be remote by zoom.

For exercises we will include a tutorial to install all the software at home.

The lab will be divided in 3 groups and students need physical assistance and can come in their dedicate time in ETZ K63.
3 Std.
Mo13:15-17:00ETZ K 63 »
M. Magno, L. Benini
227-0121-00LKommunikationssysteme Information W6 KP2V + 2U
227-0121-00 VKommunikationssysteme
Die genauen Unterrichtszeiten von ONLINE - Veranstaltungen werden von den Dozierenden kommuniziert.
2 Std.
Mi08:00-10:00ON LI NE »
A. Wittneben
227-0121-00 UKommunikationssysteme
Die genauen Unterrichtszeiten von ONLINE - Veranstaltungen werden von den Dozierenden kommuniziert.
2 Std.
Mi10:00-12:00ON LI NE »
A. Wittneben
227-0225-00LLinear System TheoryW6 KP5G
227-0225-00 GLinear System Theory
The lecturers will communicate the exact lesson times of ONLINE courses.
5 Std.
Mo09:00-12:00ON LI NE »
Mi10:00-12:00ON LI NE »
M. Colombino
227-0417-00LInformation Theory IW6 KP4G
227-0417-00 GInformation Theory I
Classroom teaching with lecture recording.
4 Std.
Mi14:15-18:00ETF C 1 »
A. Lapidoth
227-0421-00LLearning in Deep Artificial and Biological Neuronal NetworksW4 KP3G
227-0421-00 GLearning in Deep Artificial and Biological Neuronal Networks3 Std.
Mi09:15-12:00ML H 44 »
B. Grewe
227-0445-10LMathematical Methods of Signal Processing Information W6 KP4G
227-0445-10 GMathematical Methods of Signal Processing
Lecture start is on Tuesday, 6 October 2020.

Remote lecture on Tusdays 8-10h, per Zoom:
Meeting-ID: 994 133 7347
Kenncode: 530642

Remote exercise on Thursdays 14-16h, per Zoom.
Meeting-ID: 863 7709 0433
Kenncode: 170548

The lecturer will communicate the exact lesson times of ONLINE courses.
4 Std.
Di08:00-10:00ON LI NE »
Do14:00-16:00ON LI NE »
H. G. Feichtinger
227-0477-00LAcoustics IW6 KP4G
227-0477-00 GAcoustics I4 Std.
Mo14:15-18:00ETZ E 7 »
K. Heutschi
263-5210-00LProbabilistic Artificial Intelligence Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2A
263-5210-00 VProbabilistic Artificial Intelligence
The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality.
3 Std.
Fr10:15-12:00ETA F 5 »
13:15-14:00ETA F 5 »
A. Krause
263-5210-00 UProbabilistic Artificial Intelligence2 Std.
Do16:00-18:00ON LI NE »
A. Krause
263-5210-00 AProbabilistic Artificial Intelligence2 Std.A. Krause
401-0647-00LIntroduction to Mathematical Optimization Belegung eingeschränkt - Details anzeigen W5 KP2V + 1U
401-0647-00 VIntroduction to Mathematical Optimization
"Hybrid"
Online except in September/October 2020 for students in the Computational Science and Engineering Bachelor's Programme, where this course is mandatory.
Those students will be invited by the lecturer to the classroom teaching (Tue 16-18 ETH Zentrum campus).
As of November 2020 ONLINE for all students.
The lecturers will communicate the exact lesson times of ONLINE courses.
URL for live streaming: https://video.ethz.ch/live/lectures/zentrum/hg/hg-d-3-2.html
2 Std.
Di16:00-18:00ON LI NE »
D. Adjiashvili
401-0647-00 UIntroduction to Mathematical Optimization
Gruppeneinteilung erfolgt über myStudies.
Wed 12-13 or Wed 16-17

The lecturers will communicate the exact lesson times of ONLINE courses.
1 Std.
Mi12:15-13:00HG D 1.2 »
16:00-17:00ON LI NE »
16:15-17:00HG D 1.2 »
D. Adjiashvili
401-3054-14LProbabilistic Methods in Combinatorics Information W6 KP2V + 1U
401-3054-14 VProbabilistic Methods in Combinatorics2 Std.
Mi10:15-12:00HG E 5 »
B. Sudakov
401-3054-14 UProbabilistic Methods in Combinatorics1 Std.
Mo15:15-16:00HG G 3 »
B. Sudakov
401-3621-00LFundamentals of Mathematical Statistics Information W10 KP4V + 1U
401-3621-00 VFundamentals of Mathematical Statistics
The lecturers will communicate the exact lesson times of ONLINE courses.
URL for live streaming: https://video.ethz.ch/live/lectures/zentrum/hg/hg-d-5-2.html
4 Std.
Di08:00-10:00ON LI NE »
Mi10:00-12:00ON LI NE »
S. van de Geer
401-3621-00 UFundamentals of Mathematical Statistics
The lecturers will communicate the exact lesson times of ONLINE courses.
1 Std.
Di12:00-13:00ON LI NE »
S. van de Geer
401-3901-00LMathematical OptimizationW11 KP4V + 2U
401-3901-00 VMathematical Optimization
The lecturers will communicate the exact lesson times of ONLINE courses.
4 Std.
Mo14:00-16:00ON LI NE »
Do10:00-12:00ON LI NE »
R. Zenklusen
401-3901-00 UMathematical Optimization
Gruppeneinteilung erfolgt über myStudies.
Thu 14-16 or Fri 10-12 or Fr 12-14 or Fri 14-16 (depending on demand)

The lecturers will communicate the exact lesson times of ONLINE courses.
2 Std.
Do14:00-16:00ON LI NE »
Fr10:15-12:00CAB G 51 »
12:15-14:00HG E 1.2 »
14:15-16:00HG G 26.1 »
R. Zenklusen
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