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DTEND:20261113T170000Z
DESCRIPTION:## Overview\nWith the rise of new technologies\, the volume of 
 omics data in the fields of biology and medicine has grown exponentially i
 n recent times and a major issue is to mine useful predictive knowledge fr
 om these data. Machine learning (ML) is a discipline in which computer alg
 orithms perform automated learning by using data in order to assist humans
  to deal with the large volume of multidimensional data. The analysis of s
 uch data is not trivial and ML is a necessary tool to extract knowledge an
 d make predictions that can advance the field of bioinformatics. \n\nThis 
 2-day course will introduce participants to common ML algorithms and teach
  how to apply them to omics data in extensive practical sessions. The prac
 tical sessions will be conducted in R based on the tidymodels ML framework
 . The course will comprise a number of hands-on exercises and challenges w
 here the participants will acquire a first understanding of the standard M
 L methods and processes\, as well as the practical skills in applying them
  to real world problems using publicly available biological or medical dat
 a sets. \n## Audience\nThis course is designed for PhD students\, postdoct
 oral and other researchers in the life sciences from both academia and ind
 ustry who are interested in applying ML to analyse their data\, omics or o
 therwise. \n\n## Learning outcomes\nAt the end of the course\, the partici
 pants are expected to:\n* Understand the ML taxonomy and the commonly used
  machine learning algorithms for analysing “omics” data \n* Understand
  differences between ML approaches and in which situations they can be app
 lied \n* Understand and critically evaluate applications of ML in omics st
 udies \n* Learn how to implement common ML algorithms using the tidymodels
  framework \n* Interpret and visualize the results obtained from ML analys
 es \n\n## Prerequisites\n##### Knowledge / competencies\nFamiliarity with 
 the R programming language is required for this course\, as well as some b
 asic knowledge on statistics. Knowledge of the tidyverse\, dplyr syntax\, 
 and ggplot plotting is also recommended. Knowledge of different omics data
  is also recommended. \n\nAs such\, you should meet the learning outcomes 
 of [First Steps with R in Life Sciences](https://www.sib.swiss/training/co
 urse/FSWRR) and [Introduction to statistics and Data Visualisation with R]
 (https://www.sib.swiss/training/course/STATR). \n\n\n##### Technical\nA Wi
 -Fi enabled laptop with latest [R](https://www.r-project.org/) and [RStudi
 o](https://www.rstudio.com/products/rstudio/download/#download) versions i
 nstalled\, as well as a set of libraries which will be communicated prior 
 to the course. There will be access to the eduroam and guest WiFi network.
  \n\n## Schedule - CET time zone\n\nOn both days the course will start at 
 9:00 and end around 17:00. \n\nThe first day will be dedicated to introduc
 ing the data preprocessing and exploration as well as unsupervised learnin
 g (Dimensionality Reduction\, clustering) while the second day will cover 
 in more depth the topic of supervised learning (classification\, regressio
 n\, cross-validation\,...). \n\n## Application\n\nThe registration fees fo
 r academics are **200 CHF** and **1000 CHF** for for-profit companies.\n\n
 You will be informed by email of your registration confirmation. Upon rece
 ption of the confirmation email\, participants will be asked to confirm at
 tendance by paying the fees within 5 days.\n\nApplications close on *29/10
 /2026*. Deadline for free-of-charge cancellation is set to *29/10/2026*. C
 ancellation after this date will not be reimbursed. Please note that parti
 cipation in SIB courses is subject to our [general conditions](https://www
 .sib.swiss/training/terms-and-conditions).\n\n## Venue and Time\nThis cour
 se will take place at the University of Basel.\n\n\nThe course will start 
 at 9:00 CET and end around 17:00 CET.\n\n\nPrecise information will be pro
 vided to the registered participants in due time.\n\n\n## Additional infor
 mation\nCoordination: Valeria Di Cola\, SIB Training Group.\n\nHelper: Joa
 na Carlevaro.\n\nWe will recommend 0.5 ECTS credits for this course (given
  a passed exam at the end of the course).\n\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 important deadlines using the form [here](https
 ://lists.sib.swiss/mailman/listinfo/courses).\n\n\nPlease note that partic
 ipation in SIB courses is subject to our [general conditions](http://www.s
 ib.swiss/training/terms-and-conditions).\n\n\nSIB abides by the [ELIXIR Co
 de of Conduct](https://elixir-europe.org/events/code-of-conduct). Particip
 ants of SIB courses are also required to abide by the same code.\n\n\nFor 
 more information\, please contact [training@sib.swiss](mailto://training@s
 ib.swiss).
SUMMARY:Introduction to Machine Learning with R
URL;VALUE=URI:https://www.sib.swiss/training/course/20261112_INMLR
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