Hans-Andrea Loeliger: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Hans-Andrea Loeliger
FieldSignalverarbeitung
Address
Inst. f. Signal-u.Inf.verarbeitung
ETH Zürich, ETF E 101
Sternwartstrasse 7
8092 Zürich
SWITZERLAND
Telephone+41 44 632 27 65
E-mailloeliger@isi.ee.ethz.ch
URLhttp://people.ee.ethz.ch/~loeliger/
DepartmentInformation Technology and Electrical Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
227-0085-12LProjects & Seminars: Electronic Circuits & Signals Exploration Laboratory Information Restricted registration - show details
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 credits1PH.‑A. Loeliger
AbstractThe 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.
ObjectiveAs everyday electronic circuits have transitioned into integrated circuits, they have become increasingly difficult to examine and to tinker with. As a result, students become less exposed to basic analog electronic circuits and their fundamental operating principles. At university level, bachelor classes in analog circuits and electronics provide rigorous theoretical insights but are typically focused on linearised operating behaviour.

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.
227-0085-22LProjects & Seminars: Programming of a Blackfin DSP Information Restricted registration - show details
Does not take place this semester.
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.
4 credits4PH.‑A. Loeliger
AbstractThe 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.
ObjectiveMany practical applications require the processing of digital signals in real time (e.g., digital communication, audio and video processing, radar, etc.). Digital Signal Processors (DSPs) are a family of microprocessors specifically designed and optimized for this purpose.

In this course, students learn the basics of digital signal processing as well as how to implement them on DSPs with assembler. The relevant theory and the necessary skills in assembler programming will be acquired step by step. The course culminates in an individual small project which students carry out in groups of two.

The course uses a custom-designed board for implementation. The board features components as they are also common in industry. It has analog inputs and outputs, an analog/digital-digital/analog codec, a DSP of the "Blackfin" family by Analog Devices (BF532) as well as 32MB of memory.
227-0101-AALDiscrete-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 credits8RH.‑A. Loeliger
AbstractThe 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.
ObjectiveThe 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.
Content1. 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 notesLecture Notes.
227-0101-00LDiscrete-Time and Statistical Signal Processing Information 6 credits4GH.‑A. Loeliger
AbstractThe course introduces 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.
ObjectiveThe 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.
Content1. 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 notesLecture Notes
227-0105-00LIntroduction to Estimation and Machine Learning Restricted registration - show details 6 credits4GH.‑A. Loeliger
AbstractMathematical basics of estimation and machine learning, with a view towards applications in signal processing.
ObjectiveStudents master the basic mathematical concepts and algorithms of estimation and machine learning.
ContentReview 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 notesLecture notes will be handed out as the course progresses.
Prerequisites / Noticesolid basics in linear algebra and probability theory
227-0427-00LSignal Analysis, Models, and Machine Learning
Does not take place this semester.
This course was replaced by "Introduction to Estimation and Machine Learning" and "Advanced Signal Analysis, Modeling, and Machine Learning".
6 credits4GH.‑A. Loeliger
AbstractMathematical methods in signal processing and machine learning.
I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.
II. Learning linear and nonlinear functions and filters: neural networks, kernel methods.
III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.
ObjectiveThe course is an introduction to some basic topics in signal processing and machine learning.
ContentPart I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis.
Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods.
Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.
Lecture notesLecture notes.
Prerequisites / NoticePrerequisites:
- local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.)
- others: solid basics in linear algebra and probability theory