BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260805T060617Z UID:d25ee476-1805-490f-8811-708730941024 DTSTART:20260601T090000Z DTEND:20260622T170000Z DESCRIPTION:Please note that this 2-day course will be streamed over 4 half -days\, in the afternoon of the following dates:\n* 01 June 2026\n* 08 Jun e 2026\n* 15 June 2026\n* 22 June 2026\n\n\n## Overview\nWith the rise of new technologies\, the volume of omics data in biology and medicine has gr own exponentially recently. A significant issue is to mine useful predicti ve knowledge from these data. Machine learning (ML) is a discipline in whi ch computer algorithms perform automated learning by using data to assist humans in dealing with large volumes of multidimensional data. The analysi s of such data is not trivial\, and ML is a necessary tool to extract know ledge and make predictions that can advance the field of bioinformatics.\n \nThis 2-day course will introduce participants to common ML algorithms an d how to apply them to omics data in extensive practical sessions. The pra ctical sessions will be conducted in Python3 based on the widely applied s cikit-learn ML framework. The course will comprise a number of hands-on ex ercises and challenges where the participants will acquire a first underst anding of the standard ML methods and processes\, as well as the practical skills in applying them to real world problems using publicly available b iological or medical data sets. \n\n## Audience\nThis course is designed f or PhD students\, postdoctoral and other researchers in the life sciences from both academia and industry who are interested in applying ML to analy ze these data.\n\n## Learning objectives\nAt the end of the course\, the p articipants should be able to:\n* **Understand** the ML taxonomy and the c ommonly used machine learning algorithms for analysing “omics” data\n* **Understand** differences between ML approaches and in which situations they can be applied\n* **Understand** and critically **evaluate** applicat ions of ML in omics studies\n* **Learn** how to implement common ML algori thms using the scikit-learn Python framework \n* **Interpret** and **visua lize** the results obtained from ML analyses\n\n## Prerequisites\n### Know ledge / competencies\nFamiliarity with the Python programming language and pandas data frames\, as well as a basic knowledge on statistics is requir ed. Before applying to this course\, please assess your Python and statist ics skills using the quiz [here](https://forms.gle/ZpQFyHHwoPQKJSwv7).\n\n No prior knowledge of ML concepts and methods is required. Knowledge of di fferent omics data is recommended.\n\nThis course is part of the [Machine Learning](https://www.sib.swiss/training/learning-paths?path=machine-learn ing) learning path. To get the most out of this course\, you should meet t he learning outcomes of [First Steps with Python in Life Sciences](https:/ /www.sib.swiss/training/course/FSWPY) and [Introduction to statistics with R](https://www.sib.swiss/training/course/STATR) course(s). Upon completio n of this course\, you may wish to attend the [Ensuring More Accurate\, Ge neralisable\, and Interpretable Machine Learning Models for Bioinformatics ](https://www.sib.swiss/training/course/INTML)\, [Diving into Deep Learnin g - Theory and Applications with PyTorch](https://www.sib.swiss/training/c ourse/DEEPP) and [Federated Learning in Bioinformatics](https://www.sib.sw iss/training/course/FEDBX) courses.\n\n\n### Technical\n\nYou will need ac cess to a computer with a recent python3 as well as a number of python lib raries installed. Please follow these [instructions to setup your environm ent ](https://github.com/sib-swiss/intro-machine-learning-training/blob/ma in/env_setup.md)(note: these instructions use [conda](https://docs.conda.i o/projects/conda/en/latest/user-guide/install/index.html) to manage the di fferent packages) \n\nPlease perform these installations PRIOR to the cour se and contact us if you have any trouble. \n\n\n## Application\nThe regis tration fees for academics are **200 CHF** and **1000 CHF** for for-profit companies.\n\nWhile participants are registered on a first come\, first s erved basis\, exceptions may be made to ensure diversity and equity\, whic h may increase the time before your registration is confirmed.\n\nApplicat ions will close on **18/05/2026** or as soon as the places will be filled up. Cancellation after **18/05/2026** will not be reimbursed. Please note that participation in SIB courses is subject to our [general conditions](h ttp://www.sib.swiss/training/terms-and-conditions).\n\nYou will be informe d by email of your registration confirmation. Upon reception of the confir mation email\, participants will be asked to confirm attendance by paying the fees within **5 working days**.\n\n## Venue and Time\nPlease note that this 2-day course will be streamed over 4 half-days\, from 13:00 to 17:00 CEST on the following dates:\n* 01 June 2026\n* 08 June 2026\n* 15 June 2 026\n* 22 June 2026\n\nPrecise information will be provided to the registe red participants in due time. \n\n## Additional information\nCoordination : Diana Marek\, SIB Training group.\n\nA **Certificate of Attendance** wil l be sent provided you were present at the course\, whereas a **Certificat e of Achievement** recommending [X] ECTS will be sent provided you passed the exam. \n\nYou are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops\, as well as all import ant deadlines using the form [here](https://lists.sib.swiss/mailman/listin fo/courses).\n\nSIB abides by the [ELIXIR Code of Conduct](https://elixir- europe.org/events/code-of-conduct). Participants of SIB courses are also r equired to abide by the same code.\n\nFor more information\, please contac t [training@sib.swiss](mailto:training@sib.swiss). SUMMARY:Introduction to Machine Learning with Python URL;VALUE=URI:https://www.sib.swiss/training/course/20260601_INMLP END:VEVENT END:VCALENDAR