227-0105-00L Introduction to Estimation and Machine Learning
| Semester | Autumn Semester 2020 |
| Lecturers | H.‑A. Loeliger |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 227-0105-00 G | Introduction to Estimation and Machine Learning
 The lecturers will communicate the exact lesson times of ONLINE courses. | 4 hrs |
| H.‑A. Loeliger |
Catalogue data
| Abstract | Mathematical basics of estimation and machine learning, with a view towards applications in signal processing. |
| Objective | Students master the basic mathematical concepts and algorithms of estimation and machine learning. |
| Content | Review of probability theory; basics of statistical estimation; least squares and linear learning; Hilbert spaces; Gaussian random variables; singular-value decomposition; kernel methods, neural networks, and more |
| Lecture notes | Lecture notes will be handed out as the course progresses. |
| Prerequisites / Notice | solid basics in linear algebra and probability theory |
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
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| General |  : Special students and auditors need a special permission from the lecturers |
| Priority | Registration for the course unit is only possible for the primary target group |
| Primary target group | Mechanical Engineering BSc (152000)
Robotics, Systems and Control MSc (159000) Micro- and Nanosystems MSc (161000) Mechanical Engineering MSc (162000) Electrical Engin. + Information Technology BSc (228000) Quantum Engineering MSc (235000) Energy Science and Technology MSc (236000) Energy Science and Technology MSc (236000) Electrical Engin. + Information Technology MSc (237000) Biomedical Engineering MSc (238000) Computer Science BSc (252000) Data Science MSc (261000) Computer Science MSc (263000) DAS ETH in Data Science (266000) Mathematics BSc (404000) Physics BSc (405000) Computational Science and Engineering BSc (406000) Mathematics MSc (437000) Applied Mathematics MSc (437100) Computational Science and Engineering MSc (438000) Physics MSc (460000) Neural Systems and Computation MSc (461100) |
