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Advanced Signal Analysis, Modeling, and Machine Learning
Last Updated: 2026-02-05 15:55:09
Abstract
The course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms.
Objective
The course develops a selection of topics pivoting around factor graphs, state space methods, and pertinent algorithms: - factor graphs and message passing algorithms - hidden-Markov models - linear state space models, Kalman filtering, and recursive least squares - Gaussian message passing - Gibbs sampling, particle filter - recursive local polynomial fitting & applications - parameter learning by expectation maximization - sparsity and spikes - binary control and digital-to-analog conversion - duality and factor graph transforms
Resources
Lecture Notes
Lecture notes
Learning Materials (Links)
General Information
- Language
- English
- Levels
- DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 180 minutes
- Aids
- Lecture Notes (not including problems and solutions) and personal notes (max. 4 pages). No electronic devices. (Pocket calculators will be handed out, if necessary.)
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Advanced Signal Analysis, Modeling, and Machine Learning |
|
4 h weekly |
Offered In
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Track Core Courses (During the Master program, a minimum of 12 CP must be obtained from track core courses.)
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Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area.)
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Communication (The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Communication", see . The individual study plan is subject to the tutor's approval.)
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Specialization Courses (These specialization courses are particularly recommended for the area of "Communication", 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 Master's Programme.)
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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 . The individual study plan is subject to the tutor's approval.)
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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.)
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Major Courses (A total of 42 CP must be achieved form courses during the Master Program. The individual study plan is subject to the tutor's approval.)
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Core Subjects (These core subjects are particularly recommended for the field of "Communication".)
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Doctoral Dep. of Information Technology and Electrical Engineering (More Information at: )
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Doctoral and Post-Doctoral Courses (A minimum of 12 ECTS credit points must be obtained during doctoral studies. The courses on offer below are but a small selection out of a much larger available number of courses. Please discuss your course selection with your PhD supervisor.)
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Electives (This is a selection of courses particularly suitable for the MSc QE. In agreement with the tutor, students may choose other courses from the ETH course catalogue.)
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