Introduction to Machine Learning in Plant Sciences - Module 1
| 2024_HS_MachLearnInPS_Mod1 | |
| 20.11.2024 - 22.11.2024 | |
| 3 days | |
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Start registration period: 03.06.2024 End of registration period: 05.11.2024 |
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| ETH Zurich, centre | |
| 2 | |
| 16 | |
| Students enrolled in PSC PhD Programs: CHF 0 LSZGS PhD students: CHF 0 All others: CHF 300 | |
| ETH, tba | |
| This course will introduce machine learning with emphasis on plant sciences. In Module 1 we will discuss topics like data pre-processing, feature extraction, clustering, regression, and classification. In Module 2, we will take first steps towards modern deep learning. Both modules consist of 50% lectures and 50% hands-on programming in python, where students will directly implement learned theory as a software to help solving problems in plant sciences. Students with a non-technical background will be introduced to machine learning. Emphasis is on hands-on programming and implementation of basic machine learning concepts to demystify the subject, equip participants with all necessary insights and tools to develop their own solutions, and to come up with original ideas for problems related to the context of plant sciences. Specific importance is placed upon the reconciliation of the predictions, which have been generated by automated processes, with the realities. By the end of the course, students will be able to decide where (and where not) to use machine learning, what method to choose for what research task, and how to critically evaluate model outputs in the context of plant sciences. | |
| Prof. Dr. Jan Dirk Wegner (ETHZ) | |
| 1 | |
| Open for PhD students. Priority will be given to PhD students of the PhD programs in Plant Sciences, Science & Policy and Ecology. Postdocs if places available. | |
| Students should bring their laptops to the exercises because we will program on laptops directly. It is required that students enrolling in this course have successfully passed a course in basic data science and are familiar with programming (preferably in Python). Teaching assistants will help with all programming exercises. | |
| English | |
| Graded excercises | |
By registering you agree to the PSC course terms and conditions AGBs | |
| Cancellation of a course registration should be arranged with the course coordination office psc_phdprogram@ethz.ch and is possible free of charge up to 2 weeks before the course starts. Later cancellations and failure to attend or incomplete attendance without documented justification will incur a fee of 200 CHF. | |
| Module 1 is a prerequisite for taking Module 2. To attend Module 2, please register separately in “Introduction to Machine Learning in Plant Sciences Module 2” | |
| Dr. Bojan Gujas (psc_phdprogram@ethz.ch) | |
| BG_HS24_IntroMachLearn 1.pdfvertical_align_bottom |