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DTSTAMP:20260615T034338Z
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DTSTART:20231009T000000Z
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DESCRIPTION:# Overview\nWith the rise of new technologies\, the volume of o
 mics data in the fields of biology and medicine has grown exponentially in
  recent times and a major issue is to mine useful predictive knowledge fro
 m these data. Machine learning (ML) is a discipline in which computer algo
 rithms perform automated learning by using data in order to assist humans 
 to deal with the large volume of multidimensional data. The analysis of su
 ch data is not trivial and ML is a necessary tool to extract knowledge and
  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 pract
 ical sessions will be conducted in Python3 based on the widely applied sci
 kit-learn ML framework. The course will comprise a number of hands-on exer
 cises and challenges where the participants will acquire a first understan
 ding of the standard ML methods and processes\, as well as the practical s
 kills in applying them to real world problems using publicly available bio
 logical or medical data sets. \n\n# Audience\nThis course is intended for 
 PhD students\, post-docs and staff scientists who are interested in applyi
 ng ML to analyze these data.\n\n# Learning objectives\nAt the end of the c
 ourse\, the participants are expected to:\n* Understand the ML taxonomy an
 d the commonly used machine learning algorithms for analysing “omics” 
 data\n* Understand differences between ML approaches and in which situatio
 ns they can be applied\n* Understand and critically evaluate applications 
 of ML in omics studies\n* Learn how to implement common ML algorithms usin
 g the scikit-learn Python framework \n* Interpret and visualize the result
 s obtained from ML analyses\n\n# Prerequisites\n### ***Knowledge / compete
 ncies***\nNo prior knowledge of ML concepts and methods is required. \n\nK
 nowledge of different -omics data is recommended.\n\nFamiliarity with the 
 Python programming language and pandas dataframes as well as a basic knowl
 edge on statistics is required. \n\nThe competences and knowledge levels r
 equired correspond to those taught in courses such as: [First Steps with P
 ython in Life Sciences](https://www.sib.swiss/training/website/course/2023
 0301_PYTFS)\, [Introduction to statistics with Python](https://www.sib.swi
 ss/training/website/course/20230620_STATP) 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. While participants
  are registered on a first come\, first served basis\, exceptions may be m
 ade to ensure diversity and equity\, which may increase the time before yo
 ur registration is confirmed.\n\nYou will be informed by email of your reg
 istration confirmation. Upon reception of the confirmation email\, partici
 pants will be asked to confirm attendance by paying the fees within 5 days
 .\n\nDeadline for registration and free-of-charge cancellation is set is s
 et to **25/09/2023**. Cancellation after this date will not be reimbursed.
  Please note that participation to SIB courses is subject to our [general 
 conditions](http://www.sib.swiss/training/terms-and-conditions).\n\n# Venu
 e and Time\nThis course will take place in Zurich at the Chemistry | Biolo
 gy | Pharmacy Information Center. \n\nThe course will start at 9:00 CET an
 d end around 17:00 CET. \n\nPrecise information will be provided to the pa
 rticipants in due time.\n\n#  Additional information\nCoordination: Valeri
 a Di Cola\, 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 we
 lcome to register to the SIB courses mailing list to be informed of all fu
 ture courses and workshops\, as well as all important deadlines using the 
 form [here](https://lists.sib.swiss/mailman/listinfo/courses).\n\nSIB abid
 es by the [ELIXIR Code of Conduct](https://elixir-europe.org/events/code-o
 f-conduct). Participants of SIB courses are also required to abide by the 
 same code.\n\nFor more information\, please contact [training@sib.swiss](m
 ailto:training@sib.swiss).\n\n\n\n![](https://infozentrum.ethz.ch/typo3con
 f/ext/theme/Resources/Public/Logos/logo-retina.png)
SUMMARY:Introduction to Machine Learning with Python
URL;VALUE=URI:https://www.sib.swiss/training/course/20231009_INMLP
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