Hans-Andrea Loeliger: Catalogue data in Autumn Semester 2022

Name	Prof. Dr. Hans-Andrea Loeliger
Field	Signalverarbeitung
Address	Inst. f. Signal-u.Inf.verarbeitung ETH Zürich, ETF E 101 Sternwartstrasse 7 8092 Zürich SWITZERLAND
Telephone	+41 44 632 27 65
E-mail	loeliger@isi.ee.ethz.ch
URL	http://people.ee.ethz.ch/~loeliger/
Department	Information Technology and Electrical Engineering
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
227-0085-12L	Projects & Seminars: Electronic Circuits & Signals Exploration Laboratory Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable.	2 credits	1P	H.‑A. Loeliger
Abstract	The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
Learning objective	The goal of this lab course is for the students to enhance their understanding on how basic analog electronic circuits work, or perhaps don't work, and provide enough practical experience for the students to feel at ease using transistors, resistors, capacitors, diodes, etc., to create working circuits. For example, students create circuits that make physical quantities audible. Students are encourage to realise their own circuit ideas.
227-0101-AAL	Discrete-Time and Statistical Signal Processing Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.	6 credits	8R	H.‑A. Loeliger
Abstract	The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.
Learning objective	The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.
Content	1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm.
Lecture notes	Lecture Notes.
227-0101-00L	Discrete-Time and Statistical Signal Processing	6 credits	4G	H.‑A. Loeliger
Abstract	The course is about some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.
Learning objective	The course is about some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.
Content	1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm.
Lecture notes	Lecture Notes
227-0105-00L	Introduction to Estimation and Machine Learning	6 credits	4G	H.‑A. Loeliger
Abstract	Mathematical basics of estimation and machine learning, with a view towards applications in signal processing.
Learning 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
Lecture notes	Lecture notes will be handed out as the course progresses.
Prerequisites / Notice	solid basics in linear algebra and probability theory