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227-0427-10L 6 Credits DR , MSC , WBZ D-HEST , D-MAVT , D-MATH , D-PHYS , D-INFK , D-ITET

Model-Based Estimation and Signal Analysis

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

Does not take place this semester.

VVZ CR n/a

Last Updated: 2026-06-03 00:14:06

Abstract

The course develops a selection of topics pivoting around state space models, factor graphs, and pertinent algorithms for estimation, model fitting, and learning.

Objective

The course develops a selection of topics pivoting around state space methods, factor graphs, and pertinent algorithms: - hidden-Markov models - factor graphs and message passing algorithms - linear state space models, Kalman filtering, and recursive least squares - Gibbs sampling, particle filter - recursive local polynomial fitting for signal analysis - parameter learning by expectation maximization - linear-model fitting beyond least squares: sparsity, Lp-fitting and regularization, jumps - binary, M-level, and half-plane constraints in control and communications

Resources

Lecture Notes

Lecture notes

Learning Materials (Links)

Main link
https://isi.ee.ethz.ch/teaching/courses/mesa.html

General Information

Language: English
Levels: DR , MSC , WBZ
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.)

Please note that the examination will be offered only twice, in August 2025 and Feb 2026.

Course Components

Type	Title	Time & Place	Hours
lecture with exercise	Model-Based Estimation and Signal Analysis Does not take place this semester. Offered for the last time in 2025	No time listed	4 h weekly

Offered In

Biomedical Engineering Master (Only courses offered under "GESS Science in Perspective" count in this category. See "Offered in" tab in course view. For more information, please refer to )
- Major Courses
  - Bioelectronics
    - Track Core Courses (During the Master program, a minimum of 12 CP must be obtained from track core courses.)
Mathematics Master
- 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. Credits from other application areas cannot be recognised for further application areas.)
  - Information and Communication Technology
Electrical Engineering and Information Technology Master
- 01 - Courses of specializations
  - Track: 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.)
    - 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
    - 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. Semester / Research Projects are not allowed in this category. A minimum of 40 credits must be obtained from specialization courses during the Master's Programme.)
  - Track: 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.)
    - 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
  - Track: Biomedical Engineering (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Biomedical Engineering", see . The individual study plan is subject to the tutor's approval.)
    - Core Courses (These core courses are particularly recommended for the field of "Biomedical Engineering" 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.)
Neural Systems and Computation Master
- Electives
Doctorate Information Technology and Electrical Engineering (A minimum of 12 ECTS credit points must be obtained during doctoral studies (also see sub-categories for details) More Information at )
- Subject Specialisation (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.)
Data Science Master
- Master Studies (Programme Regulations 2017)
  - Core Courses
    - Core Electives
- Master Studies (Programme Regulations 2023)
  - Electives
    - Subject-Specific Electives
DAS in Data Science
- Specialisation Track
  - Machine Learning and Artificial Intelligence
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.)