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DESCRIPTION:**This course is now full with a long waiting list.**  If you d
 o not want to miss your chance to be part of the next session and remain i
 nformed about all training activities at SIB\, we highly recommend you to 
 keep an eye on our list of [upcoming events](https://www.sib.swiss/trainin
 g/upcoming-training-courses) and subscribe to our courses [mailing list](h
 ttps://lists.sib.swiss/mailman/listinfo/courses) here (if you haven't done
  so already).\n\n\n**Please note that this 2-day course will be streamed o
 ver 4 half-days\, in the afternoon of the following dates:	**\n* 5 June 20
 23\n* 12 June 2023\n* 19 June 2023\n* 26 June 2023\n\n\n\n# Overview\nWith
  the rise of new technologies\, the volume of omics data in biology and me
 dicine has grown exponentially recently. A significant issue is to mine us
 eful predictive knowledge from these data. Machine learning (ML) is a disc
 ipline in which computer algorithms perform automated learning by using da
 ta to assist humans in dealing with large volumes of multidimensional data
 . The analysis of such data is not trivial\, and ML is a necessary tool to
  extract knowledge and make predictions that can advance the field of bioi
 nformatics.\n\nThis 2-day course will introduce participants to common ML 
 algorithms and how to apply them to omics data in extensive practical sess
 ions. The practical sessions will be conducted in Python3 based on the wid
 ely applied scikit-learn ML framework. The course will comprise a number o
 f hands-on exercises and challenges where the participants will acquire a 
 first understanding of the standard ML methods and processes\, as well as 
 the practical skills in applying them to real world problems using publicl
 y available biological or medical data sets. \n\n# Audience\nThis course i
 s intended for PhD students\, post-docs and staff scientists who are inter
 ested in applying ML to analyze these data.\n\n# Learning objectives\nAt t
 he end of the course\, the participants are expected to:\n* Explain the ML
  taxonomy and the commonly used machine learning algorithms for analysing 
 omics data\n* Describe differences between ML approaches and in which situ
 ations they can be applied\n* Critically evaluate applications of ML in om
 ics studies\n* Implement common ML algorithms using the scikit-learn Pytho
 n framework \n* Interpret and visualize the results obtained from ML analy
 ses\n\n# Prerequisites\n### ***Knowledge / competencies***\nNo prior knowl
 edge of ML concepts and methods is required. \n\nKnowledge of different -o
 mics data is recommended.\n\nFamiliarity with the Python programming langu
 age and pandas dataframes as well as a basic knowledge on statistics is re
 quired. \n\nThe competences and knowledge levels required correspond to th
 ose taught in courses such as: [First Steps with Python in Life Sciences](
 https://www.sib.swiss/training/website/course/20230301_PYTFS)\, [Introduct
 ion to statistics with Python](https://www.sib.swiss/training/website/cour
 se/20230620_STATP) and [Introduction to statistics with R](https://www.sib
 .swiss/training/website/course/20230206_STATR). \nTest your skills with Py
 thon and statistics with the quiz [here](https://forms.gle/ZpQFyHHwoPQKJSw
 v7)\, before registering.\n\n### ***Technical***\nThis course will be stre
 amed\, you are thus required to have your own computer with an Internet co
 nnection.\n\nAdditionally\, you will need to have a recent python3 as well
  as a number of python libraries installed. Please follow these [instructi
 ons to setup your environment ](https://github.com/sib-swiss/intro-machine
 -learning-training/blob/main/env_setup.md)(note: these instructions use [c
 onda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/in
 dex.html) to manage the different packages) \n\nPlease perform these insta
 llations PRIOR to the course and contact us if you have any trouble. \n\nF
 inally\, although not mandatory\, we also highly recommend you to use the 
 same computer to connect to the zoom classroom and perform the exercises\,
  otherwise we will have difficulties helping you debug your code. \n\n# Ap
 plication\nThe registration fees for academics are **200 CHF** and **1000 
 CHF** for for-profit companies. While participants are registered on a fir
 st come\, first served basis\, exceptions may be made to ensure diversity 
 and equity\, which may increase the time before your registration is confi
 rmed.\n\nYou will be informed by email of your registration confirmation. 
 Upon reception of the confirmation email\, participants will be asked to c
 onfirm attendance by paying the fees within 5 days.\n\nApplications will c
 lose on **1/05/2023** or as soon as the places will be filled up. Deadline
  for free-of-charge cancellation is set to **22/05/2023**. Cancellation af
 ter this date will not be reimbursed. Please note that participation to SI
 B courses is subject to our [general conditions](http://www.sib.swiss/trai
 ning/terms-and-conditions).\n\n# Venue and Time\nThis course will be strea
 med. \n\n**Please note that this 2-day course will be streamed over 4 half
 -days\, from 14:00 to 17:30 CET of the following dates:	**\n* 5 June 2022\
 n* 12 June 2022\n* 19 June 2022\n* 26 June 2022\n\nPrecise information wil
 l be provided to the participants in due time.\n\n#  Additional informatio
 n\nCoordination: Patricia Palagi\, SIB Training Group.\n\nWe will recommen
 d 0.50 ECTS credits for this course (given a passed exam at the end of the
  course).\n\nYou are welcome to register to the SIB courses mailing list t
 o be informed of all future courses and workshops\, as well as all importa
 nt deadlines using the form [here](https://lists.sib.swiss/mailman/listinf
 o/courses).\n\nSIB abides by the [ELIXIR Code of Conduct](https://elixir-e
 urope.org/events/code-of-conduct). Participants of SIB courses are also re
 quired to abide by the same code.\n\nFor more information\, please contact
  [training@sib.swiss](mailto:training@sib.swiss).
SUMMARY:Introduction to Machine Learning with Python
URL;VALUE=URI:https://www.sib.swiss/training/course/20230605_INMLP
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