Search result: Catalogue data in Autumn Semester 2022
Agricultural Sciences Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Data Science and Technology for Agricultural Science | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 credits | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | ● 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | 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 credits | 1G | M. Mächler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 *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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W+ | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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. 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 credits | 2G | S. Mintchev | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Copies of the slides and exercises will be provided on the course Moodle page. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | 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 | GIS - Introduction into Geoinformation Science and Technology Number of participants limited to 75. Waiting list will be deleted 07.10.2022. | W+ | 5 credits | 2V + 3P | M. A. M. Niederhuber | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Theoretical basics and fundamental concepts of Geographic Information Science (GIS) are imparted and subsequently further elaborated with the software ArcGIS. At the end, the students will be able to independently solve basic realistic GIS problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students are able to - elucidate the theoretical and conceptional foundations of geographic information systems (GIS) - independently perform normal GIS work using commercial software and practical examples | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course covers the following topics: - What is GIS? What are spatial data? - The representation of reality by means of spatial data models: vector, raster, TIN - The four phases of data modelling: Spatial, conceptual, logical and physical model - Possibilities of data collection - Transition of reference frame - Spatial Analysis I: query and manipulation of vector data - Spatial Analysis II: operators and functions with raster data - Digital elevation models and derived products - Process modelling with vector and raster data - Presentation possibilities of spatial data One Friday is reserved for a field trip or guest speaker; | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | 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 Number of participants limited to 60. | W+ | 3 credits | 4G | A. Baltensweiler, M. Hägeli-Golay | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Knowledge of the basic architecture and spatial data handling capabilities of geographic information systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Introduction to Geographic Information Systems, Tutorial: Introduction to ArcGIS Pro | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | 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 credits | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | • 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Building on existing data science resources | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Math IV, VI (Statistics); R, Python; ESDS I | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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