BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260615T023653Z UID:2c0e5f9c-08de-4ac3-a94c-3a279f3554eb DTSTART:20240527T090000Z DTEND:20240527T170000Z DESCRIPTION:**This course is now full with a long waiting list.** If you d o not want to miss your chance to be part of the next session and remain i nformed about all training activities at SIB\, we highly recommend you to keep an eye on our list of [upcoming events](https://www.sib.swiss/trainin g/upcoming-training-courses) and subscribe to our courses [mailing list](h ttps://lists.sib.swiss/mailman/listinfo/courses) here (if you haven't done so already).\n\nPlease note that this 2-day course will be streamed over 4 half-days\, in the afternoon of the following dates:\n* 27 May 2024\n* 3 June 2024\n* 10 June 2024\n* 17 June 2024\n\n\n# Overview\nWith the rise of new technologies\, the volume of omics data in biology and medicine has grown exponentially recently. A significant issue is to mine useful predi ctive knowledge from these data. Machine learning (ML) is a discipline in which computer algorithms perform automated learning by using data to assi st humans in dealing with large volumes of multidimensional data. The anal ysis of such data is not trivial\, and ML is a necessary tool to extract k nowledge and make predictions that can advance the field of bioinformatics .\n\nThis 2-day course will introduce participants to common ML algorithms and how to apply them to omics data in extensive practical sessions. The practical sessions will be conducted in Python3 based on the widely applie d scikit-learn ML framework. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first unde rstanding of the standard ML methods and processes\, as well as the practi cal skills in applying them to real world problems using publicly availabl e biological or medical data sets. \n\n# Audience\nThis course is intended for PhD students\, post-docs and staff scientists who are interested in a pplying ML to analyze these data.\n\n# Learning objectives\nAt the end of the course\, the participants are expected to:\n* Explain the ML taxonomy and the commonly used machine learning algorithms for analysing omics data \n* Describe differences between ML approaches and in which situations the y can be applied\n* Critically evaluate applications of ML in omics studie s\n* Implement common ML algorithms using the scikit-learn Python framewor k \n* Interpret and visualize the results obtained from ML analyses\n\n# P rerequisites\n### ***Knowledge / competencies***\nNo prior knowledge of ML concepts and methods is required. \n\nKnowledge of different omics data i s recommended.\n\nFamiliarity with the Python programming language and pan das data frames as well as a basic knowledge on statistics is required. \n \nThe competences and knowledge levels required correspond to those taught in courses such as: [First Steps with Python in Life Sciences](https://ww w.sib.swiss/training/course/20240304_FSWP) and [Introduction to statistics with R](https://www.sib.swiss/training/website/course/20230206_STATR). \n Test your skills with Python and statistics with the quiz [here](https://f orms.gle/ZpQFyHHwoPQKJSwv7)\, before registering.\n\n### ***Technical***\n This course will be streamed\, you are thus required to have your own comp uter with an Internet connection.\n\nAdditionally\, you will need to have a recent python3 as well as a number of python libraries installed. Please follow these [instructions to setup your environment ](https://github.com /sib-swiss/intro-machine-learning-training/blob/main/env_setup.md)(note: t hese instructions use [conda](https://docs.conda.io/projects/conda/en/late st/user-guide/install/index.html) to manage the different packages) \n\nPl ease perform these installations PRIOR to the course and contact us if you have any trouble. \n\nFinally\, although not mandatory\, we also highly r ecommend you to use the same computer to connect to the zoom classroom and perform the exercises\, otherwise we will have difficulties helping you d ebug your code. \n\n# Application\nThe registration fees for academics are **200 CHF** and **1000 CHF** for for-profit companies.\n\nWhile participa nts are registered on a first come\, first served basis\, exceptions may b e made to ensure diversity and equity\, which may increase the time before your registration is confirmed.\n\nApplications will close on **20/05/202 4** or as soon as the places will be filled up. Deadline for free-of-charg e cancellation is set to **20/05/2024**. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subje ct to our [general conditions](https://www.sib.swiss/training/terms-and-co nditions).\n\nYou will be informed by email of your registration confirmat ion. Upon reception of the confirmation email\, participants will be asked to confirm attendance by paying the fees within 5 days.\n\n# Venue and Ti me\nPlease note that this 2-day course will be streamed over 4 half-days\, from 14:00 to 17:30 CET of the following dates:\n* 27 May 2024\n* 3 June 2024\n* 10 June 2024\n* 17 June 2024\n\nMore information will be provided to the registered participants one week before the course starts. \n\n# A dditional information\nCoordination: Monique Zahn\, SIB Training group.\n\ nWe will recommend 0.50 ECTS credits for this course (given a passed exam at the end of the course).\n\nYou are welcome to register to the SIB cours es mailing list to be informed of all future courses and workshops\, as we ll as all important deadlines using the form [here](https://lists.sib.swis s/mailman/listinfo/courses).\n\nSIB abides by the [ELIXIR Code of Conduct] (https://elixir-europe.org/events/code-of-conduct). Participants of SIB co urses are also required to abide by the same code.\n\nFor more information \, please contact [training@sib.swiss](mailto:training@sib.swiss). SUMMARY:Introduction to Machine Learning with Python URL;VALUE=URI:https://www.sib.swiss/training/course/20240527_INMLP END:VEVENT END:VCALENDAR