Suchergebnis: Katalogdaten im Herbstsemester 2022
Agrarwissenschaften Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Data Science and Technology for Agricultural Science | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-3001-00L | Environmental Systems Data Science: Data Processing  **Students who have taken 701-3001-00L Environmental Systems Data Science in autumn semester 2020 or 21 are not allowed to take this lecture. The content is similar.** Number of participants is limited to 80. Course registration starts on 31.08.2022. Priority is given to the target groups until 23.09.2022, Target groups Agricultural Sciences MSc Environmental Sciences MSc Atmospheric and Climate Science MSc Environmental Sciences PhD Agricultural Sciences PhD Waiting list will be deleted on 30.09.2022 | W+ | 2 KP | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Students are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students are able to ● frame a data science problem and build a hypothesis ● describe the steps of a typical data science project workflow ● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models ● critically think about the limits and implications of a method ● visualise data and results throughout the workflow ● access online resources to keep up with the latest data science methodology and deepen their understanding | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | ● The data science workflow ● Access and handle (large) datasets ● Prepare and clean data ● Analysis: data exploratory steps ● Analysis: machine learning and computational methods ● Evaluate results and analyse uncertainty ● Visualisation and communication | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | 252-0840-02L Anwendungsnahes Programmieren mit Python 401-0624-00L Mathematik IV: Statistik 401-6215-00L Using R for Data Analysis and Graphics (Part I) 401-6217-00L Using R for Data Analysis and Graphics (Part II) 701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W+ | 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=18279 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W+ | 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=15522 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 751-5510-00L | Introduction to Agricultural Robotics Number of participants limited to 30. | W+ | 3 KP | 2G | S. Mintchev | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Autonomous robots are quickly becoming a key player in the transition to precision agriculture. In this course, students will learn theoretical and practical aspects of robotics. Lectures will introduce how robots operate and analyse their application to precision agriculture. In hands-on laboratories, students will apply concepts learned in class on educational robots to simulate a weeding task. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After the course, students will be able to critically examine and select appropriate robotic solutions for agricultural applications. The learning objectives of the course are: (i) illustrate the principle of operation of the main components of a robotic system, (ii) analyse how the different robotic components are integrated and contribute to the functioning of a robotic system, and (iii) solve problems in the field of agriculture using robotic principles. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Robots are becoming a key technology in the transition to smart farming and in supporting the agricultural needs of the 21st century. For example, robots enable site-specific fertilization, automated weeding, or livestock herding. The course gives an overview of robotic systems, beginning with their fundamental components (e.g., sensors, actuators, locomotion strategies) and gradually scaling up to the system level, illustrating the concepts of perception, robot control, obstacle avoidance and navigation. Exercises performed with an educational robot (Thymio) will complement the theoretical lectures providing a hands-on practical experience of the challenges of using these machines. During the course, students will gradually apply the theoretical and practical knowledge they are learning. To this end, students will work in teams to develop a robotic solution for an agricultural task of their choice. Students will learn to translate the task into meaningful requirements for a robotic system and critically select the most appropriate components to achieve the required robotic functions. Students will periodically present and discuss the development of this "robot design" exercise during presentations and in a journal report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Copies of the slides and exercises will be provided on the course Moodle page. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | - A. Bechar and C. Vigneault, “Agricultural robots for field operations: Concepts and components,” Biosyst. Eng., vol. 149, pp. 94–111, 2016. - S. Asseng and F. Asche, “Future farms without farmers,” Sci. Robot., vol. 4, no. 27, p. eaaw1875, Feb. 2019. - D. C. Rose, J. Lyon, A. de Boon, M. Hanheide, and S. Pearson, “Responsible development of autonomous robotics in agriculture,” Nat. Food, vol. 2, no. 5, pp. 306–309, 2021. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | No mandatory prerequisites, but it is preferable that students have a basic knowledge of computer programming. Class size limitation to 30 students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-0951-00L | GIST - Einführung in die räumlichen Informationswissenschaften und -technologien Die Teilnehmerzahl ist auf 75 Studierende beschränkt. Die Warteliste wird am 07.10.2022 gelöscht. | W+ | 5 KP | 2V + 3P | M. A. M. Niederhuber | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Im Kurs werden theoretische Grundlagen und Konzepte der Geoinformationswissenschaften (GIS) vermittelt und mit der Software ArcGIS umgesetzt. Die Studierenden sind nach Abschluss in der Lage, selbstständig einfache, reale GIS-Probleme zu lösen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Die Studierenden können - theoretische und konzeptionelle Grundlagen von Geographischen Inforamtionssystemen (GIS) erläutern. - alltägliche GIS-Arbeiten mit einer kommerziellen Software an Praxis-Beispielen selbst durchführen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Im Rahmen des Kurses werden folgende Themen behandelt: - Was ist ein GIS? Was sind räumliche Daten? - Die Abbildung der Realität mittels räumlichen Datenmodellen: Vektor, Raster, TIN - Die 4 Phasen der Datenmodellierung: Räumliches, konzeptionelles, logisches und physikalisches Modell - Möglichkeiten der Datenerfassung - Referenzrahmenwechsel - Räumliche Analyse I: Abfrage und Manipulation von Vektordaten - Räumliche Analyse II: Operatoren und Funktionen mit Rasterdaten - Digitale Höhenmodelle und daraus abgeleitete Produkte - Prozessmodellierung mit Vektor- und Rasterdaten - Präsentationsmöglichkeiten räumlicher Daten Ein Vorlesungstermin ist für eine Exkursion oder Gastvortrag reserviert; | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Paul A. Longley, Michael F. Goodchild, David J. Maguire, David W. Rhind (2010): Geographic Information Systems and Science. John Wiley & Son, Ltd. Chichester. Norbert Bartelme (2005): Geoinformatik - Modelle, Strukturen, Funktionen. Springer Verlag. Heidelberg. Ralf Bill (2010): Grundlagen der Geo-Informationssysteme. 5., völlig neu bearbeitete Auflage. Wichmann Verlag. Heidelberg. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Aufgrund der Grösse des verfügbaren EDV-Schulungsraumes ist die Teilnehmerzahl auf 50 Studierende beschränkt! Für die Übungen werden die Studierenden auf zwei, max. drei Zeitfenster aufgeteilt. Pro Zeitfenster können maximal 25 Studierende betreut werden. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 651-4031-00L | Geographic Information Systems Maximale Teilnehmerzahl: 60 | W+ | 3 KP | 4G | A. Baltensweiler, M. Hägeli-Golay | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to the architecture and data processing capabilities of geographic information systems (GIS). Practical application of spatial data modeling and geoprocessing functions to a selected project from the earth sciences. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Knowledge of the basic architecture and spatial data handling capabilities of geographic information systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Theoretical introduction to the architecture, modules, spatial data types and spatial data handling functions of geographic information systems (GIS). Application of data modeling principles and geoprocessing capabilities using ArcGIS: Data design and modeling, data acquisition, data integration, spatial analysis of vector and raster data, particular functions for digital terrain modeling and hydrology, map generation and 3D-visualization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Introduction to Geographic Information Systems, Tutorial: Introduction to ArcGIS Pro | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Longley, P. A., M. F. Goodchild, D. J. Maguire, and D. W. Rhind (2015): Geographic Information Systems and Science. Fourth Edition. John Wiley & Sons, Chichester, England. Peter A. Burrough, Rachael A. McDonnell and Christopher D. Lloyd (2015): Principles of Geographical Information Systems. Third edition. Oxford : Oxford University Press, England. De Smith, Michael J., Michael F. Goodchild, and Paul A. Longley. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. 6th edition. London: Drumlin Security, 2018. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-3003-00L | Environmental Systems Data Science: Machine Learning Number of participants is limited to 80. Course registration starts on 31.08.2022. Priority is given to the target groups until 23.09.2022, Target groups Agricultural Sciences MSc Environmental Sciences MSc Atmospheric and Climate Science MSc Environmental Sciences PhD Agricultural Sciences PhD Waiting list will be deleted on 30.09.2022 | W+ | 3 KP | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Students are introduced to advanced data science where environmental data are analyzed using state of the art machine learning methods. Starting from known statistical approaches, they learn the principle of more advanced machine learning methods with practical application. The course enables students to plan their own data science project in their specialization and to apply machine learning mode | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students are able to • select an appropriate model related to a research question and dataset • describe the steps from data preparation to running and evaluating models • prepare data for running machine learning with dependent and independent variable • build and validate regressions and neural network models • understand convolution and deep learning models • access online resources to keep up with the latest data science methodology and deepen their understanding | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | • The data science workflow • Data preparation for running and validating machine learning models • Get to know machine learning approaches including regression, random forest and neural network • Model complexity and hyperparameters • Model parameterization and loss • Model evaluations and uncertainty • Deep learning with convolutions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Building on existing data science resources | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Math IV, VI (Statistics); R, Python; ESDS I | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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