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227-0427-00L 6 Credits DR , MSC D-USYS , D-BAUG , D-MAVT , D-INFK , D-MTEC , D-MATH , D-PHYS , D-BIOL , D-GESS , D-ITET , D-ARCH , D-CHAB , D-HEST
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Signal and Information Processing: Modeling, Filtering, Learning

Lecturers & Examiners: Prof. Dr. Hans-Andrea Loeliger
VVZ CR n/a

Last Updated: 2026-02-05 15:14:58

Abstract

The course is an introduction to some basic topics in signal processing and machine learning: Hilbert spaces, LMMSE estimation and filtering, filter banks and wavelets, singular value decomposition, adaptive filters, neural networks, kernel methods, hidden Markov models, Kalman filtering, factor graphs.

Objective

The course is an introduction to some basic topics in signal processing, adaptive filters, detection/estimation theory, and machine learning.

Content

Part I - Linear Signal Representation and Approximation: Hilbert spaces, orthogonality principle, wavelets and filter banks, SVD, LMMSE estimation and filtering, adaptive filters. Part II - Learning Nonlinear Functions: neural networks, kernel methods. Part III - Algorithms for Structured Models: factor graphs, hidden Markov models and trellises, Kalman filtering and related topics, EM algorithm.

Resources

Lecture Notes

Lecture notes.

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
oral 30 minutes

Course Components

Type Title Time & Place Hours
lecture with exercise Signal and Information Processing: Modeling, Filtering, Learning
  • Fri 08:15-12:00 (ETZ E 8)
4 h weekly

Offered In