## Hans-Andrea Loeliger: Catalogue data in Autumn Semester 2020 |

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 |

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. | ||||

Objective | As everyday electronic circuits have transitioned into integrated circuits, they have become increasingly difficult to examine and 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, capacitances, 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. | ||||

227-0085-22L | Projects & Seminars: Programmierung eines Blackfin DSP 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 credits | 4P | 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. | ||||

Objective | Die Echtzeitverarbeitung von digitalen Signalen ist eine Herausforderung welche in der Praxis häufig auftritt (digitale Kommunikation, Audio- und Videovearbeitung, ...). Es gibt eine Familie von Mikroprozessoren welche spezifisch für die Echtzeitverarbeitung von digitalen Signalen optimiert sind: Sogenannte "Digital Signal Processor" oder kurz DSP. In diesem Praktikum lernt ihr einige Grundlagen der digitalen Signalverarbeitung und deren Implementation auf einem DSP kennen. In Zweiergruppen werdet ihr euch am Beispiel von akustischen Signalen Schritt für Schritt an die Theorie und die Programmierung in Assembler herantasten. In der zweiten Hälfte des Semesters könnt ihr ein kleines, selbst bestimmtes Audio-Projekt verwirklichen. Für die Implementierung verwenden wir ein für dieses P&S entwickeltes Board mit Komponenten welche auch in der Industrie verwendet werden. Es ist bestückt mit Ein- und Ausgängen für analoge Audiosignale, einem Codec, welcher das analoge Signal in ein digitales und zurück umwandelt, einem DSP der Familie "Blackfin" von Analog Devices (BF532) und 32MB Arbeitsspeicher. | ||||

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. | ||||

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 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. | ||||

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 | ||||

227-0105-00L | 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. | ||||

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 | ||||

Lecture notes | Lecture notes will be handed out as the course progresses. | ||||

Prerequisites / Notice | solid basics in linear algebra and probability theory | ||||

227-0427-00L | Signal Analysis, Models, and Machine LearningDoes not take place this semester. This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester). | 6 credits | 4G | H.‑A. Loeliger | |

Abstract | Mathematical 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. | ||||

Objective | The course is an introduction to some basic topics in signal processing and machine learning. | ||||

Content | Part 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 notes | Lecture notes. | ||||

Prerequisites / Notice | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | ||||

401-5680-00L | Foundations of Data Science Seminar | 0 credits | P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, G. Rätsch, C. Uhler, S. van de Geer, F. Yang | ||

Abstract | Research colloquium | ||||

Objective |