## Martin Mächler: Catalogue data in Autumn Semester 2020 |

Name | Prof. em. Dr. Martin Mächler |

Address | Seminar für Statistik (SfS) ETH Zürich, HG GO 14.2 Rämistrasse 101 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 34 08 |

maechler@stat.math.ethz.ch | |

URL | http://stat.ethz.ch/~maechler |

Department | Mathematics |

Relationship | Retired Adjunct Professor |

Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|

401-3622-00L | Statistical Modelling | 8 credits | 4G | P. L. Bühlmann, M. Mächler | |

Abstract | In regression, the dependency of a random response variable on other variables is examined. We consider the theory of linear regression with one or more covariates, high-dimensional linear models, nonlinear models and generalized linear models, robust methods, model choice and nonparametric models. Several numerical examples will illustrate the theory. | ||||

Objective | Introduction into theory and practice of a broad and popular area of statistics, from a modern viewpoint. | ||||

Content | In der Regression wird die Abhängigkeit einer beobachteten quantitativen Grösse von einer oder mehreren anderen (unter Berücksichtigung zufälliger Fehler) untersucht. Themen der Vorlesung sind: Einfache und multiple Regression, Theorie allgemeiner linearer Modelle, Hoch-dimensionale Modelle, Ausblick auf nichtlineare Modelle. Querverbindungen zur Varianzanalyse, Modellsuche, Residuenanalyse; Einblicke in Robuste Regression. Durchrechnung und Diskussion von Anwendungsbeispielen. | ||||

Lecture notes | Lecture notes | ||||

Prerequisites / Notice | This is the course unit with former course title "Regression". Credits cannot be recognised for both courses 401-3622-00L Statistical Modelling and 401-0649-00L Applied Statistical Regression in the Mathematics Bachelor and Master programmes (to be precise: one course in the Bachelor and the other course in the Master is also forbidden). | ||||

401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, M. Robinson, C. Strobl, C. Uhler, S. van de Geer | |

Abstract | About 5 talks on applied statistics. | ||||

Objective | See how statistical methods are applied in practice. | ||||

Content | There will be about 5 talks on how statistical methods are applied in practice. | ||||

Prerequisites / Notice | This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web: http://stat.ethz.ch/events/zukost Course language is English or German and may depend on the speaker. | ||||

401-6215-00L | Using R for Data Analysis and Graphics (Part I) | 1.5 credits | 1G | M. Mächler | |

Abstract | The course provides the first part an introduction to the statistical 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. | ||||

Objective | The students will be able to use the software R for simple data analysis and graphics. | ||||

Content | 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. 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. | ||||

Lecture notes | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | ||||

Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform. 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=13499 ALL material is available on this moodle page. | ||||

401-6217-00L | Using R for Data Analysis and Graphics (Part II) | 1.5 credits | 1G | M. Mächler | |

Abstract | 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. | ||||

Objective | The students will be able to use the software R efficiently for data analysis, graphics and simple programming | ||||

Content | 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 | ||||

Lecture notes | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | ||||

Prerequisites / Notice | 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. 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=13500 ALL material is available on this moodle page. | ||||

447-6221-00L | Nonparametric Regression Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to Link. The Registrar's Office will then register you for the course. | 1 credit | 1G | M. Mächler | |

Abstract | This course focusses on nonparametric estimation of probability densities and regression functions. These recent methods allow modelling without restrictive assumptions such as 'linear function'. These smoothing methods require a weight function and a smoothing parameter. Focus is on one dimension, higher dimensions and samples of curves are treated briefly. Exercises at the computer. | ||||

Objective | Knowledge on estimation of probability densities and regression functions via various statistical methods. Understanding of the choice of weight function and of the smoothing parameter, also done automatically. Practical application on data sets at the computer. | ||||

447-6245-00L | Data Mining Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to Link. The Registrar's Office will then register you for the course. | 1 credit | 1G | M. Mächler | |

Abstract | Block course only on prediction problems, aka "supervised learning". Part 1, Classification: logistic regression, linear/quadratic discriminant analysis, Bayes classifier; additive and tree models; further flexible ("nonparametric") methods. Part 2, Flexible Prediction: additive models, MARS, Y-Transformation models (ACE,AVAS); Projection Pursuit Regression (PPR), neural nets. | ||||

Objective | |||||

Content | "Data Mining" is a large field from which in this block course, we only treat so called prediction problems, aka "supervised learning". Part 1, Classification, recalls logistic regression and linear / quadratic discriminant analysis (LDA/QDA) and extends these (in the framework of 'Bayes classifier") to (generalized) additive (GAM) and tree models (CART), and further mentions other flexible ("nonparametric") methods. Part 2, Flexible Prediction (of continuous or "class" response/target) contains additive models, MARS, Y-Transformation models (ACE, AVAS); Projection Pursuit Regression (PPR), neural nets. | ||||

Lecture notes | The block course is based on (German language) lecture notes. | ||||

Prerequisites / Notice | The exercises are done exlusively with the (free, open source) software "R" (http://www.r-project.org). A final exam will also happen at the computers, using R (and your brains!). |