Suchergebnis: Lerneinheiten 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 Link.

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 KP4GH.‑A. Loeliger
227-0105-00LIntroduction to Estimation and Machine Learning Belegung eingeschränkt - Details anzeigen W6 KP4GH.‑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 + 1UH. Bölcskei
227-0427-00LSignal 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This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester).
W6 KP4GH.‑A. Loeliger
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, E. Konukoglu, F. Yu
252-0535-00LAdvanced Machine Learning Information W10 KP3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
263-3210-00LDeep Learning Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2AT. 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 KP5GF. 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 KP3GM. Magno, L. Benini
227-0121-00LKommunikationssysteme Information W6 KP2V + 2UA. Wittneben
227-0225-00LLinear System TheoryW6 KP5GM. Colombino
227-0417-00LInformation Theory IW6 KP4GA. Lapidoth
227-0421-00LLearning in Deep Artificial and Biological Neuronal NetworksW4 KP3GB. Grewe
227-0445-10LMathematical Methods of Signal Processing Information W6 KP4GH. G. Feichtinger
227-0477-00LAcoustics IW6 KP4GK. Heutschi
263-5210-00LProbabilistic Artificial Intelligence Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2AA. Krause
401-0647-00LIntroduction to Mathematical Optimization Belegung eingeschränkt - Details anzeigen W5 KP2V + 1UD. Adjiashvili
401-3054-14LProbabilistic Methods in Combinatorics Information W6 KP2V + 1UB. Sudakov
401-3621-00LFundamentals of Mathematical Statistics Information W10 KP4V + 1US. van de Geer
401-3901-00LMathematical OptimizationW11 KP4V + 2UR. Zenklusen
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