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Signal Analysis, Models, and Machine Learning
Last Updated: 2026-02-05 15:35:58
Abstract
Mathematical methods in signal processing and machine learning.I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.II. Learning linear and nonlinear functions and filters: neural networks, kernel methods.III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.
Objective
The course is an introduction to some basic topics in signal processing and machine learning.
Content
Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.
Resources
Lecture Notes
Lecture notes.
General Information
- Language
- English
- Levels
- DR , BSC , MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 30 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Signal Analysis, Models, and Machine Learning
Does not take place this semester.
This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester)
|
No time listed | 4 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Track Core Courses (During the Master programme, a minimum of 12 CP must be obtained from track core courses.)
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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 . 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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Advanced Core Courses (Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.)
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Communication (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Communication", 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 "Communication". 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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Advanced Core Courses (Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.)
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Major Courses (A total of 42 CP must be achieved during the Master Programme. 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 only 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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