VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
227-0105-00L
6
Credits
BSC
,
DR
,
MSC
,
WBZ
D-MAVT
,
D-INFK
,
D-MATH
,
D-PHYS
,
D-ITET
,
D-HEST
Introduction to Estimation and Machine Learning
Lecturers & Examiners:
Prof. Dr. Ender Konukoglu
Last Updated: 2026-06-03 00:07:31
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.
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 | No time listed | 4 h weekly |
Offered In
-
-
5th semester: third year core courses (Can be freely combined, a list of detailed recommendations is available under )
-
Specialization: Biomedical Engineering (These core courses are particularly recommended for the field of "Biomedical Engineering" but students may choose core courses from all fields freely.)
-
Specialization: Communications, Control, and Signal Processing (These core courses are particularly recommended for the field of "Communications, Control, and Signal Processing" but students may choose core courses from all fields freely.)
-
-
-
-
-
-
Track Core Courses (During the Master programme, a minimum of 12 CP must be obtained from track core courses.)
-
-
-
-
-
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.)
-
-
-
-
Track: 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.)
-
Core Courses (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.)
-
-
Track: 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.)
-
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.)
-
Foundation Core Courses (Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area.)
-
-
-
Track: 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.)
-
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.)
-
-
-
-
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 only a small selection out of a much larger available number of courses. Please discuss your course selection with your PhD supervisor.)
-
-
-