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227-0427-00L 6 Credits BSC , DR , MSC D-HEST , D-MAVT , D-PHYS , D-MATH , D-ITET

Signal Analysis, Models, and Machine Learning

Lecturers & Examiners: Prof. Dr. Hans-Andrea Loeliger

Does not take place this semester. This course was replaced by "Introduction to Estimation and Machine Learning" and "Advanced Signal Analysis, Modeling, and Machine Learning".

VVZ CR n/a

Last Updated: 2026-02-05 15:48:31

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: BSC , DR , MSC

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.	No time listed	4 h weekly

Offered In

Computational Science and Engineering Bachelor
- Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
Computational Science and Engineering Master
- Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
Biomedical Engineering Master
- Track Courses
  - Bioelectronics
    - Track Core Courses (During the Master programme, a minimum of 12 CP must be obtained from track core courses.)
Electrical Engineering and Information Technology Master
- Master Studies (Programme Regulations 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 . The individual study plan is subject to the tutor's approval.)
    - 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.)
      - Advanced Core Courses (Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.)
  - 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.)
    - 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.)
      - Advanced Core Courses (Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.)
- Master Studies (Programme Regulations 2008)
  - 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.)
    - Communication
      - Core Subjects (These core subjects are particularly recommended for the field of "Communication".)
    - Signal Processing and Machine Learning
      - Core Subjects
Neural Systems and Computation Master
- Electives
Doctoral Dep. of Information Technology and Electrical Engineering (More Information at: )
- 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.)
Quantum Engineering Master
- 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.)