Search result: Catalogue data in Autumn Semester 2022
Environmental Sciences Master  | |||||||||||||||||||||
 Major in Ecology and Evolution | |||||||||||||||||||||
| Electives | |||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||
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| 701-0290-00L | Seminar in Microbial Evolution and Ecology (HS) | Z | 0 credits | 2S | S. Bonhoeffer | ||||||||||||||||
| Abstract | Seminar of the groups Molecular Microbial Ecology, Theoretical Biology, Experimental Ecology, Evolutionary Biology. Talks given by members of these groups and external visitors. | ||||||||||||||||||||
| Learning objective | In-depth introduction into microbial evolution and ecology, especially the aspects that are the focus of on-going research in this area at Department of Environmental Systems Science. | ||||||||||||||||||||
| 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 | ||||||||||||||||||||
| 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 | ||||||||||||||||||||
| 551-0205-00L | Challenges in Plant Sciences Number of participants limited to 40. | W | 2 credits | 2K | S. C. Zeeman, S. Mintchev, M. Paschke, B. Pfister, further lecturers | ||||||||||||||||
| Abstract | The colloquium “Challenges in Plant Sciences” is a core class of the Zurich-Basel Plant Science Center's PhD program and the MSc module. The colloquium introduces participants to the broad spectrum of plant sciences within the network. The course offers the opportunity to approach interdisciplinary topics in the field of plant sciences. | ||||||||||||||||||||
| Learning objective | Objectives of the colloquium are: Introduction to resecent research in all fields of plant sciences Working in interdisciplinary teams on the topics Developing presentation and discussion skills | ||||||||||||||||||||
| Content | The topics encompass integrated knowledge on current plant research, ranging from the molecular level to the ecosystem level, and from basic to applied science while making use of the synergies between the different research groups within the PSC. More information on the content: https://www.plantsciences.uzh.ch/en/teaching/masters/colloquium.html | ||||||||||||||||||||
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| 751-4504-00L | Plant Pathology I | W | 2 credits | 2G | B. McDonald | ||||||||||||||||
| Abstract | Plant Pathology I will focus on pathogen-plant interactions, epidemiology, disease assessment, and disease development in agroecosystems. Themes will include: 1) how pathogens attack plants and; 2) how plants defend themselves against pathogens; 3) factors driving the development of epidemics in agroecosystems. | ||||||||||||||||||||
| Learning objective | Students will understand: 1) how pathogens attack plants and; 2) how plants defend themselves against pathogens; 3) factors driving the development of epidemics in agroecosystems as a basis for implementing disease management strategies in agroecosystems. | ||||||||||||||||||||
| Content | Course description: Plant Pathology I will focus on pathogen-plant interactions, epidemiology, disease assessment, and disease development in agroecosystems. Themes will include: 1) how pathogens attack plants and; 2) how plants defend themselves against pathogens; 3) factors driving the development of epidemics in agroecosystems. Topics under the first theme will include pathogen life cycles, disease cycles, and an overview of plant pathogenic nematodes, viruses, bacteria, and fungi. Topics under the second theme will include plant defense strategies, host range, passive and active defenses, and chemical and structural defenses. Topics under the third theme will include the disease triangle and cultural control strategies. Lecture Topics and Tentative Schedule Week 1 The nature of plant diseases, symbiosis, parasites, mutualism, biotrophs and necrotrophs, disease cycles and pathogen life cycles. Week 2 Nematode attack strategies and types of damage. Viral pathogens, classification, reproduction and transmission, attack strategies and types of damage. Examples TMV, BYDV. Bacterial pathogens and phytoplasmas, classification, reproduction and transmission. Week 3 Bacterial attack strategies and symptoms. Example bacterial diseases: fire blight, Agrobacterium crown gall, soft rots. Fungal and oomycete pathogens, classification, growth and reproduction, sexual and asexual spores, transmission. Week 4 Fungal and oomycete life cycles, disease cycles, infection processes, colonization, phytotoxins and mycotoxins. Attack strategies of fungal necrotrophs and biotrophs. Symptoms and signs of fungal infection. Example fungal diseases: potato late blight. Week 5 Example fungal diseases: wheat stem rust, grape powdery mildew, wheat septoria tritici blotch. Plant defense mechanisms, host range and non-host resistance. Passive structural and chemical defenses, preformed chemical defenses. Active structural defense, histological and cellular (papillae). Week 6 Active chemical defense, hypersensitive response, pathogenesis-related (PR) proteins, phytoalexins and disease resistance. Pisatin and pisatin demethylase. Local and systemic acquired resistance (LAR, SAR), induced systemic resistance (ISR), signal molecules, defense activators (Bion). Pathogen effects on food quality. Positive and negative transformations. Week 7 Negative pathogen impacts on crop yield and quality. Pathogen effects on food safety. Mycotoxins in the food chain. Aflatoxin, patulin safety assessment and action thresholds. Epidemiology: historical epidemics. Week 8 Epidemiology: Disease pyramid, environmental effects on epidemic development, plant effects on development of epidemics, including resistance, physiology, density, uniformity. Week 9 Disease assessment: incidence and severity measures, keys, diagrams, scales, measurement errors. Correlations between incidence and severity. Molecular detection and diagnosis of pathogens. Host indexing, serology, monoclonal and polyclonal antibodies, ELISA. Week 10 Molecular detection and diagnosis of pathogens: PCR, rDNA and loop-mediated isothermal amplification. Strategies for minimizing disease risks: calculating disease thresholds, disease forecasting systems. Week 11 Strategies for minimizing disease risks: lowering epidemic risk, ecological risk assessment, natural and synthetic pesticides. Disease control strategies: economic thresholds, overview of control strategies. Week 12 Physical control methods. Cultural control methods: avoidance, tillage practices, crop sanitation. Week 13 Cultural control methods: fertilizers, crop rotations. Week 14 Open lecture. | ||||||||||||||||||||
| Lecture notes | Detailed lecture notes (~160 pages) will be available for purchase at the cost of reproduction at the start of the semester. | ||||||||||||||||||||
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