227-0427-10L Advanced Signal Analysis, Modeling, and Machine Learning
| Semester | Spring Semester 2021 |
| Lecturers | H.‑A. Loeliger |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 227-0427-10 G | Advanced Signal Analysis, Modeling, and Machine Learning | 4 hrs |
| H.‑A. Loeliger |
Catalogue data
| Abstract | The course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms. |
| Learning 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 |
| Lecture notes | Lecture notes |
| Prerequisites / Notice | Solid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning". |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 6 credits |
| Examiners | H.-A. Loeliger |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. |
| Mode of examination | written 180 minutes |
| Written 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.) |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| Main link | https://isi.ee.ethz.ch/teaching/courses/asml.html |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
