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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 Python3 based on the widely applied sc
 ikit-learn ML framework. The course will comprise a number of hands-on exe
 rcises and challenges where the participants will acquire a first understa
 nding of the standard ML methods and processes\, as well as the practical 
 skills in applying them to real world problems using publicly available bi
 ological or medical data sets. \n\n## Audience\nThis course is designed fo
 r PhD students\, postdoctoral and other researchers in the life sciences f
 rom both academia and industry who are interested in applying ML to analyz
 e omics data.\n\n## Learning objectives\nAt the end of the course\, the pa
 rticipants should be able to:\n* **Understand** the ML taxonomy and the co
 mmonly used machine learning algorithms for analysing “omics” data\n* 
 **Understand** differences between ML approaches and in which situations t
 hey can be applied\n* **Understand** and critically **evaluate** applicati
 ons of ML in omics studies\n* **Learn** how to implement common ML algorit
 hms using the scikit-learn Python framework \n* **Interpret** and **visual
 ize** the results obtained from ML analyses\n\n## Prerequisites\n### Knowl
 edge / competencies\n\nFamiliarity with the Python programming language an
 d pandas data frames\, as well as a basic knowledge on statistics is requi
 red. Before applying to this course\, please assess your Python and statis
 tics skills using the quiz [here](https://forms.gle/ZpQFyHHwoPQKJSwv7).\n\
 nNo prior knowledge of ML concepts and methods is required. Knowledge of d
 ifferent omics data is recommended.\n\nThis course is part of the [Machine
  Learning](https://www.sib.swiss/training/learning-paths?path=machine-lear
 ning) learning path. To get the most out of this course\, you should meet 
 the learning outcomes of [First Steps with Python in Life Sciences](https:
 //www.sib.swiss/training/course/20250311_FSWP)\n and [Introduction to stat
 istics with R](https://www.sib.swiss/training/course/20240122_STATR) cours
 e(s). Upon completion of this course\, you may wish to attend the [Ensurin
 g More Accurate\, Generalisable\, and Interpretable Machine Learning Model
 s for Bioinformatics](https://www.sib.swiss/training/course/20261117__INTM
 L)\, [Diving into Deep Learning - Theory and Applications with PyTorch](ht
 tps://www.sib.swiss/training/course/20261203_DEEPP) and [Federated Learnin
 g in Bioinformatics](https://www.sib.swiss/training/course/20260427_FEDBX)
  courses.\n\n\n### Technical\n\nYou will need to have access to a computer
  with a recent python3 as well as a number of python libraries installed. 
 Please follow these [instructions to setup your environment ](https://gith
 ub.com/sib-swiss/intro-machine-learning-training/blob/main/env_setup.md)(n
 ote: these instructions use [conda](https://docs.conda.io/projects/conda/e
 n/latest/user-guide/install/index.html) to manage the different packages) 
 \n\nPlease perform these installations PRIOR to the course and contact us 
 if you have any trouble. \n\n\n## Application\nThe registration fees for a
 cademics are **200 CHF** and **1000 CHF** for for-profit companies. \n\nWh
 ile participants are registered on a first come\, first served basis\, exc
 eptions may be made to ensure diversity and equity\, which may increase th
 e time before your registration is confirmed.\n\nApplications will close o
 n **22/09/2026**  or as soon as the places will be filled up. Cancellation
  after **22/09/2026** will not be reimbursed. Please note that participati
 on to SIB courses is subject to our [general conditions](https://www.sib.s
 wiss/training/terms-and-conditions).\n\nYou will be informed by email of y
 our registration confirmation. Upon reception of the confirmation email\, 
 participants will be asked to confirm attendance by paying the fees within
  **5 working days**.\n\n## Venue and Time\nThis course will take place at 
 the University of Bern\n\nThe course will start at 9:00 CEST and end aroun
 d 17:00 CEST. \n\nPrecise information will be provided to the participants
  in due time.\n\n##  Additional information\nCoordination: Grégoire Rossi
 er\, SIB Training Group  \n\nAt the end of the course\, we will provide a 
 *Certificate of Attendance* or a *Certificate of Achievement* recommending
  0.5 ECTS credits (given a passed exam).\n\nYou are welcome to register to
  the SIB courses mailing list to be informed of all future courses and wor
 kshops\, as well as all important deadlines using the form [here](https://
 lists.sib.swiss/postorius/lists/courses.lists.sib.swiss/).\n\nPlease note 
 that participation in SIB courses is subject to our [general conditions](h
 ttps://www.sib.swiss/training/terms-and-conditions).\n\nSIB 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\nFor more information\, please contact [training@sib.swiss](mailto:trai
 ning@sib.swiss).
SUMMARY:Introduction to Machine Learning with Python
URL;VALUE=URI:https://www.sib.swiss/training/course/20261001_INMLP
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