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Introduction to Estimation and Machine Learning
Last Updated: 2026-02-05 15:35:58
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
Resources
Lecture Notes
Lecture notes will be handed out as the course progresses.
General Information
- Language
- English
- Levels
- BSC , 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.)
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Introduction to Estimation and Machine Learning
The lecturers will communicate the exact lesson times of ONLINE courses.
|
|
4 h weekly |
Offered In
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Electives (This is only a small selection. Other courses from the ETH course catalogue may be chosen. Please consult the "Richtlinien zu Projekten, Praktika, Seminare" (German only), published on our website ( ).)
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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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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.)
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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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Foundation Core Courses (Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area.)
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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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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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