Suchergebnis: Katalogdaten im Frühjahrssemester 2022

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Elektrotechnik und Informationstechnologie Master
Master-Studium (Studienreglement 2008)
Fächer der Vertiefung Insgesamt 42 KP müssen im Masterstudium aus Vertiefungsfächern erreicht werden. Der individuelle Studienplan unterliegt der Zustimmung eines Tutors.
Signal Processing and Machine Learning
Kernfächer
227-0391-00L	Medical Image Analysis Basic knowledge of computer vision would be helpful.	W	3 KP	2G	E. Konukoglu, M. A. Reyes Aguirre
Kurzbeschreibung	It is the objective of this lecture to introduce the basic concepts used in Medical Image Analysis. In particular the lecture focuses on shape representation schemes, segmentation techniques, machine learning based predictive models and various image registration methods commonly used in Medical Image Analysis applications.
Lernziel	This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.
Voraussetzungen / Besonderes	Prerequisites: Basic concepts of mathematical analysis and linear algebra. Preferred: Basic knowledge of computer vision and machine learning would be helpful. The course will be held in English.
227-0427-10L	Advanced Signal Analysis, Modeling, and Machine Learning	W	6 KP	4G	H.‑A. Loeliger
Kurzbeschreibung	The course develops a selection of topics pivoting around state space models, factor graphs, and pertinent algorithms for estimation, model fitting, and learning.
Lernziel	The course develops a selection of topics pivoting around state space methods, factor graphs, and pertinent algorithms: - hidden-Markov models - factor graphs and message passing algorithms - linear state space models, Kalman filtering, and recursive least squares - Gibbs sampling, particle filter - recursive local polynomial fitting for signal analysis - parameter learning by expectation maximization - linear-model fitting beyond least squares: sparsity, Lp-fitting and regularization, jumps - binary, M-level, and half-plane constraints in control and communications
Skript	Lecture notes
Voraussetzungen / Besonderes	Solid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning".
227-0434-10L	Mathematics of Information	W	8 KP	3V + 2U + 2A	H. Bölcskei
Kurzbeschreibung	The class focuses on mathematical aspects of 1. Information science: Sampling theorems, frame theory, compressed sensing, sparsity, super-resolution, spectrum-blind sampling, subspace algorithms, dimensionality reduction 2. Learning theory: Approximation theory, greedy algorithms, uniform laws of large numbers, Rademacher complexity, Vapnik-Chervonenkis dimension
Lernziel	The aim of the class is to familiarize the students with the most commonly used mathematical theories in data science, high-dimensional data analysis, and learning theory. The class consists of the lecture and exercise sessions with homework problems.
Inhalt	Mathematics of Information 1. Signal representations: Frame theory, wavelets, Gabor expansions, sampling theorems, density theorems 2. Sparsity and compressed sensing: Sparse linear models, uncertainty relations in sparse signal recovery, super-resolution, spectrum-blind sampling, subspace algorithms (ESPRIT), estimation in the high-dimensional noisy case, Lasso 3. Dimensionality reduction: Random projections, the Johnson-Lindenstrauss Lemma Mathematics of Learning 4. Approximation theory: Nonlinear approximation theory, best M-term approximation, greedy algorithms, fundamental limits on compressibility of signal classes, Kolmogorov-Tikhomirov epsilon-entropy of signal classes, optimal compression of signal classes 5. Uniform laws of large numbers: Rademacher complexity, Vapnik-Chervonenkis dimension, classes with polynomial discrimination
Skript	Detailed lecture notes will be provided at the beginning of the semester.
Voraussetzungen / Besonderes	This course is aimed at students with a background in basic linear algebra, analysis, statistics, and probability. We encourage students who are interested in mathematical data science to take both this course and "401-4944-20L Mathematics of Data Science" by Prof. A. Bandeira. The two courses are designed to be complementary. H. Bölcskei and A. Bandeira
227-0449-00L	Seminar in Biomedical Image Computing	W	1 KP	2S	E. Konukoglu, B. Menze, M. A. Reyes Aguirre
Kurzbeschreibung	This is a seminar lecture focusing on recent research topics in biomedical image computing, machine learning techniques related to interpreting biomedical images and medical data in general. Every week a different topic will be presented and discussed.
Lernziel	The goal of this lecture is to provide a glimpse of the current research landscape to graduate students who are interested in working on biomedical image computing and related areas. Different topics will be covered by different speakers every week to provide a broad perspective and highlight current challenges. Every week students will be asked to read a paper, prepare discussion questions and participate in the discussion. Upon completion of this course, students will have a broad overview of the recent developments in biomedical image computing and ability to critically discuss a scientific article.
Voraussetzungen / Besonderes	Knowledge in computer vision, machine learning and biomedical image analysis would be essential.
252-0220-00L	Introduction to Machine Learning Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch	W	8 KP	4V + 2U + 1A	A. Krause, F. Yang
Kurzbeschreibung	The course introduces the foundations of learning and making predictions based on data.
Lernziel	The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Inhalt	- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
Literatur	Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Voraussetzungen / Besonderes	Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning"

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