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227-0105-00L 6 Credits BSC , DR , MSC , WBZ D-HEST , D-MAVT , D-MATH , D-PHYS , D-ITET , D-INFK

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Introduction to Estimation and Machine Learning

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

VVZ CR n/a

Last Updated: 2026-06-01 11:30:43

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; singular-value decomposition; kernel methods, neural networks, and more

Resources

Lecture Notes

Lecture notes will be handed out as the course progresses.

Learning Materials (Links)

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

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

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type	Title	Time & Place	Hours
lecture with exercise	Introduction to Estimation and Machine Learning	Fri 14:15-18:00 (ETF C 1)	4 h weekly

Offered In

Elektrotechnik und Informationstechnologie Bachelor
- 5. Semester: Kernfächer des 3. Jahres (Kurswahl kann frei zusammengestellt werden, eine Liste mit detaillierten Empfehlungen findet sich unter )
  - Vertiefung: Biomedizinische Technik (Diese Kernfächer werden insbesondere für den Bereich "Biomedizinische Technik" empfohlen, aber die Studierenden können Kernfächer aus allen Bereichen frei wählen.)
  - Vertiefung: Kommunikation, Regelung und Signalverarbeitung (Diese Kernfächer werden insbesondere für den Bereich "Kommunikation, Regelung und Signalverarbeitung" empfohlen, aber die Studierenden können Kernfächer aus allen Bereichen frei wählen.)
Biomedical Engineering Master
- Vertiefungsfächer
  - Bioelectronics
    - Kernfächer der Vertiefung (Während des Studiums müssen mindestens 12 KP aus Kernfächern einer Vertiefung (Track) erreicht werden.)
Mathematik Master
- Anwendungsgebiet (Nur für das Master-Diplom in Angewandter Mathematik erforderlich und anrechenbar. In der Kategorie Anwendungsgebiet für den Master in Angewandter Mathematik muss eines der zur Auswahl stehenden Anwendungsgebiete gewählt werden. Im gewählten Anwendungsgebiet müssen mindestens 8 KP erworben werden. Kreditpunkte aus anderen Anwendungsgebieten sind nicht für weitere Anwendungsgebiete anrechenbar.)
  - Information and Communication Technology
Elektrotechnik und Informationstechnologie Master
- 01 - Fächer der Vertiefungen
  - Vertiefung: Computers and Networks (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Computers and Networks", see . The individual study plan is subject to the tutor's approval.)
    - Kernfächer (These core courses are particularly recommended for the field of "Computers and Networks". 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
  - Vertiefung: 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.)
    - Kernfächer (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.)
      - Foundation Core Courses (Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area.)
      - Advanced Core Courses
  - Vertiefung: 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.)
    - Kernfächer (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
Doktorat Informationstechnologie und Elektrotechnik (A minimum of 12 ECTS credit points must be obtained during doctoral studies (also see sub-categories for details) More Information at )
- Vertiefung Fachwissen (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.)
DAS in Data Science
- Kernfächer
  - Einführungskurse