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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/FSWPY) and [Introduction to statistics wit
 h R](https://www.sib.swiss/training/course/STATR) course(s). Upon completi
 on of this course\, you may wish to attend the [Ensuring More Accurate\, G
 eneralisable\, and Interpretable Machine Learning Models for Bioinformatic
 s](https://www.sib.swiss/training/course/INTML)\, [Diving into Deep Learni
 ng - Theory and Applications with PyTorch](https://www.sib.swiss/training/
 course/DEEPP) and [Federated Learning in Bioinformatics](https://www.sib.s
 wiss/training/course/FEDBX) courses.\n\n\n### Technical\n\nYou will need a
 ccess to a computer with a recent python3 as well as a number of python li
 braries installed. Please follow these [instructions to setup your environ
 ment ](https://github.com/sib-swiss/intro-machine-learning-training/blob/m
 ain/env_setup.md)(note: these instructions use [conda](https://docs.conda.
 io/projects/conda/en/latest/user-guide/install/index.html) to manage the d
 ifferent packages) \n\nPlease perform these installations PRIOR to the cou
 rse and contact us if you have any trouble. \n\n\n## Application\nThe regi
 stration fees for academics are **200 CHF** and **1000 CHF** for for-profi
 t companies. \n\nWhile participants are registered on a first come\, first
  served basis\, exceptions may be made to ensure diversity and equity\, wh
 ich may increase the time before your registration is confirmed.\n\nApplic
 ations will close on **22/09/2026**  or as soon as the places will be fill
 ed up. Cancellation after **22/09/2026** will not be reimbursed. Please no
 te that participation to SIB courses is subject to our [general conditions
 ](https://www.sib.swiss/training/terms-and-conditions).\n\nYou will be inf
 ormed by email of your registration confirmation. Upon reception of the co
 nfirmation email\, participants will be asked to confirm attendance by pay
 ing 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
  CET and end around 17:00 CEST. \n\nPrecise information will be provided t
 o the registered participants in due time.  \n\n##  Additional information
 \nCoordination: Grégoire Rossier\, SIB Training Group  \n\nA **Certificat
 e of Attendance** will be sent provided you were present at the course\, w
 hereas a **Certificate 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 important deadlines using the form [here](https://lists.sib
 .swiss/postorius/lists/courses.lists.sib.swiss/).\n\nSIB abides by the [EL
 IXIR Code of Conduct](https://elixir-europe.org/events/code-of-conduct). P
 articipants of SIB courses are also required to abide by the same code.\n\
 nFor more information\, please contact [training@sib.swiss](mailto:trainin
 g@sib.swiss).
SUMMARY:Introduction to Machine Learning with Python
URL;VALUE=URI:https://www.sib.swiss/training/course/20261001_INMLP
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