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DTSTART:20261112T090000Z
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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* **Unde
 stand** differences between ML approaches and in which situations they can
  be applied \n* **Undestand** and critically **evaluate** applications of 
 ML in omics studies \n* **Learn** how to implement common ML algorithms us
 ing the tidymodels framework \n* **Interpret** and **visualize** the resul
 ts obtained from ML analyses \n\n## Prerequisites\n##### Knowledge / compe
 tencies\nFamiliarity with the R programming language is required for this 
 course\, as well as some basic knowledge on statistics. Knowledge of the t
 idyverse\, dplyr syntax\, and ggplot plotting is also recommended. Knowled
 ge of different omics data is also recommended. \n\nTo get the most out of
  this course\, you should meet the learning outcomes of [First Steps with 
 R in Life Sciences](https://www.sib.swiss/training/course/FSWRR) and [Intr
 oduction to statistics and Data Visualisation with R](https://www.sib.swis
 s/training/course/STATR). \n\n\n##### Technical\nA Wi-Fi enabled laptop wi
 th latest [R](https://www.r-project.org/) and [RStudio](https://www.rstudi
 o.com/products/rstudio/download/#download) versions installed\, 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 - CE
 T time zone\n\nOn both days the course will start at 9:00 and end around 1
 7:00. \n\nThe first day will be dedicated to introducing the data preproce
 ssing and exploration as well as unsupervised learning (Dimensionality Red
 uction\, clustering) while the second day will cover in more depth the top
 ic of supervised learning (classification\, regression\, cross-validation\
 ,...). \n\n## Application\n\nThe registration fees for academics are **200
  CHF** and **1000 CHF** for for-profit companies.\n\nYou will be informed 
 by email of your registration confirmation. Upon reception of the confirma
 tion email\, participants will be asked to confirm attendance by paying th
 e fees within 5 days.\n\nApplications close on *29/10/2026*. Deadline for 
 free-of-charge cancellation is set to *29/10/2026*. Cancellation after thi
 s date will not be reimbursed. Please note that participation in SIB cours
 es is subject to our [general conditions](https://www.sib.swiss/training/t
 erms-and-conditions).\n\n## Venue and Time\nThis course will take place at
  the University of Basel.\n\n\nThe course will start at 9:00 CET and end a
 round 17:00 CET.\n\n\nPrecise information will be provided to the register
 ed participants in due time.\n\n\n## Additional information\nCoordination:
  Valeria Di Cola\, SIB Training Group.\n\n\nWe will recommend 0.5 ECTS cre
 dits for this course (given a passed exam at the end of the course).\n\n\n
 You 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 participation in SIB courses is subject to our [gener
 al conditions](http://www.sib.swiss/training/terms-and-conditions).\n\n\nS
 IB abides by the [ELIXIR Code of Conduct](https://elixir-europe.org/events
 /code-of-conduct). Participants of SIB courses are also required to abide 
 by the same code.\n\n\nFor more information\, please contact [training@sib
 .swiss](mailto://training@sib.swiss).
SUMMARY:Introduction to Machine Learning with R
URL;VALUE=URI:https://www.sib.swiss/training/course/20261112_INMLR
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