Joshua Payne: Catalogue data in Autumn Semester 2022 |
| Name | Dr. Joshua Payne |
| Department | Environmental Systems Science |
| Relationship | Assistant Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 701-1460-00L | Ecology and Evolution: Term Paper  | 5 credits | 11A | T. Städler, J. Alexander, S. Bonhoeffer, T. Crowther, A. Hall, J. Hille Ris Lambers, J. Jokela, J. Payne, G. Velicer, A. Widmer | |
| Abstract | Individual writing of an essay-type review paper about a specialized topic in the field of ecology and evolution, based on substantial reading of original literature and discussions with a senior scientist. | ||||
| Learning objective | - Students acquire a thorough knowledge on a topic in which they are particularly interested - They learn to assess the relevance of original literature and synthesize information - They make the experience of becoming "experts" on a topic and develop their own perspective - They practise academic writing according to professional standards in English | ||||
| Content | Topics for the essays are proposed by the professors and lecturers of the major in Ecology and Evolution at a joint meeting at the beginning of the semester (the date will be communicated by e-mail to registered students). Students will: - choose a topic - search and read appropriate literature - develop a personal view on the topic and structure their arguments - prepare figures and tables to represent ideas or illustrate them with examples - write a clear, logical and well-structured text - refine the text and present the paper according to professional standards In all steps, they will benefit from the advice and detailed feedback given by a senior scientist acting as personal tutor of the student. | ||||
| Lecture notes | Reading of articles in scientific journals | ||||
| Prerequisites / Notice | The "Term Paper" requires considerable time set aside to read and digest original scientific literature, culminating in the writing of a review paper. The submission deadline is the first day of the spring semester, implying that much of the actual writing will be performed in January and February. Grading is based on the quality of the submitted review paper (2/3 of total grade), and on the "soft skills" such as the level of initiative, timeliness, independence, etc. of the student (1/3 of total grade). The personal supervisor is charged with grading the student's performance. | ||||
| 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 | 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 | ||||
| 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 | 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 | ||||