Martin Mächler: Katalogdaten im Herbstsemester 2023 |
| Name | Herr Prof. em. Dr. Martin Mächler |
| Namensvarianten | Martin Maechler |
| Adresse | Seminar für Statistik (SfS) ETH Zürich, HG GO 14.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 34 08 |
| maechler@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~maechler |
| Departement | Mathematik |
| Beziehung | Titularprofessor im Ruhestand |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||
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| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics  | 0 KP | 1K | M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl | ||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Etwa 3 Vorträge zur angewandten Statistik. | |||||||||||||||||||||||||||||||||||||||||
| Lernziel | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten. | |||||||||||||||||||||||||||||||||||||||||
| Inhalt | In etwa 3 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. | |||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm: http://stat.ethz.ch/events/zukost Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. | |||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-6215-00L | Using R for Data Analysis and Graphics (Part I)  | 1.5 KP | 1G | M. Mächler | ||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course provides the first part an introduction to the statistical/graphical/data science software R (https://www.r-project.org/) for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects. | |||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students will be able to use the software R for simple data analysis and graphics. | |||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R. Part I of the course covers the following topics: - What is R? - R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics; - Types of data: numeric, character, logical and categorical data, missing values; - Simple (statistical) functions: summary, mean, var, etc., simple statistical tests; - Writing simple functions; - Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots. The course focuses on practical work at the computer with R. We will make use of the graphical user interface RStudio: www.rstudio.org Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I. | |||||||||||||||||||||||||||||||||||||||||
| Skript | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | |||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The course resources will be provided via the Moodle web learning platform. Subscribing via Mystudies *automatically* makes you a student participant of the Moodle course of this lecture, which is at https://moodle-app2.let.ethz.ch/course/view.php?id=20847 | |||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-6217-00L | Using R for Data Analysis and Graphics (Part II) | 1.5 KP | 1G | M. Mächler | ||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions. Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known. | |||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students will be able to use the software R efficiently for data analysis, graphics and simple programming | |||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course provides the second part of an introduction to the statistical software R (https://www.r-project.org/) for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R. Part II of the course builds on part I and covers the following additional topics: - Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects; - More on R functions; - Applying functions to elements of vectors, matrices and lists; - Object oriented programming with R: classes and methods; - Tayloring R: options - Extending basic R: packages The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org | |||||||||||||||||||||||||||||||||||||||||
| Skript | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | |||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Basic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course. The course resources will be provided via the Moodle web learning platform. As from FS 2019, subscribing via Mystudies should *automatically* make you a student participant of the Moodle course of this lecture, which is at https://moodle-app2.let.ethz.ch/course/view.php?id=20848 | |||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 447-6221-00L | Nichtparametrische Regression Findet dieses Semester nicht statt. Fachstudierende "Universität Zürich (UZH)" im Master-Studiengang Biostatistik von der UZH können diese Lerneinheit nicht direkt in myStudies belegen. Leiten Sie die schriftliche Teilnahmebewilligung des Dozenten an die Kanzlei weiter. Als Einverständnis gilt auch ein direktes E-Mail des Dozenten an kanzlei@ethz.ch. Die Kanzlei wird anschliessend die Belegung vornehmen. | 1 KP | 1G | M. Mächler | ||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Fokus ist die nichtparametrische Schätzung von Wahrscheinlichkeitsdichten und Regressionsfunktionen. Diese neueren Methoden verzichten auf einschränkende Modellannahmen wie 'lineare Funktion'. Sie benötigen eine Gewichtsfunktion und einen Glättungsparameter. Schwerpunkt ist eine Dimension, mehrere Dimensionen und Stichproben von Kurven werden kurz behandelt. Übungen am Computer. | |||||||||||||||||||||||||||||||||||||||||
| Lernziel | Kenntnisse der Schätzung von Wahrscheinlichkeitsdichten und Regressionsfunktionen mittels verschiedener statistischer Methoden. Verständnis für die Wahl der Gewichtsfunktion und des Glättungsparameters, auch automatisch. Praktische Anwendung auf Datensätze am Computer. | |||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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