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DTSTAMP:20260407T174832Z
UID:3a90910e-28bd-4d2a-9dae-581f85cfb346
DTSTART:20260601T090000Z
DTEND:20260622T170000Z
DESCRIPTION:Please note that this 2-day course will be streamed over 4 half
 -days\, in the afternoon of the following dates:\n* 01 June 2026\n* 08 Jun
 e 2026\n* 15 June 2026\n* 22 June 2026\n\n\n# Overview\nWith the rise of n
 ew technologies\, the volume of omics data in biology and medicine has gro
 wn exponentially recently. A significant issue is to mine useful predictiv
 e knowledge from these data. Machine learning (ML) is a discipline in whic
 h computer algorithms perform automated learning by using data to assist h
 umans in dealing with large volumes of multidimensional data. The analysis
  of such data is not trivial\, and ML is a necessary tool to extract knowl
 edge and make predictions that can advance the field of bioinformatics.\n\
 nThis 2-day course will introduce participants to common ML algorithms and
  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 for
  PhD students\, postdoctoral and other researchers in the life sciences fr
 om both academia and industry who are interested in applying ML to analyze
  these data.\n\n# Learning objectives\nAt the end of the course\, the part
 icipants are expected to:\n* Explain the ML taxonomy and the commonly used
  machine learning algorithms for analysing omics data\n* Describe differen
 ces between ML approaches and in which situations they can be applied\n* C
 ritically evaluate applications of ML in omics studies\n* Implement common
  ML algorithms using the scikit-learn Python framework \n* Interpret and v
 isualize the results obtained from ML analyses\n\n# Prerequisites\n### ***
 Knowledge / competencies***\nThis course is part of the [Machine Learning]
 (https://www.sib.swiss/training/learning-paths?path=machine-learning) lear
 ning path. To get the most out of this course\, you should meet the learni
 ng outcomes of [First Steps with Python in Life Sciences](https://www.sib.
 swiss/training/course/FSWPY) and [Introduction to statistics and Data Visu
 alisation with R](https://www.sib.swiss/training/course/STATR) courses. Up
 on completion of this course\, you may wish to attend the [Ensuring More A
 ccurate\, Generalisable\, and Interpretable Machine Learning Models for Bi
 oinformatics](https://www.sib.swiss/training/course/INTML) course\, or the
  [Diving into Deep Learning - Theory and Applications with PyTorch](https:
 //www.sib.swiss/training/course/DEEPP) course\, or the [Federated Learning
  in Bioinformatics](https://www.sib.swiss/training/course/FEDBX) course.\n
 \nFamiliarity with the Python programming language and pandas data frames\
 , as well as a basic knowledge on statistics is required. Before applying 
 to this course\, please assess your Python and statistics skills using the
  quiz [here](https://forms.gle/ZpQFyHHwoPQKJSwv7).\n\nNo prior knowledge o
 f ML concepts and methods is required. Knowledge of different omics data i
 s recommended.\n\n### ***Technical***\nThis course will be streamed\, you 
 are thus required to have your own computer with an Internet connection.\n
 \nAdditionally\, you will need to have a recent python3 as well as a numbe
 r of python libraries installed. Please follow these [instructions to setu
 p your environment ](https://github.com/sib-swiss/intro-machine-learning-t
 raining/blob/main/env_setup.md)(note: these instructions use [conda](https
 ://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) t
 o manage the different packages) \n\nPlease perform these installations PR
 IOR to the course and contact us if you have any trouble. \n\nFinally\, al
 though not mandatory\, we also highly recommend you to use the same comput
 er to connect to the zoom classroom and perform the exercises\, otherwise 
 we will have difficulties helping you debug your code. \n\n# Application\n
 The registration fees for academics are **200 CHF** and **1000 CHF** for f
 or-profit companies.\n\nWhile participants are registered on a first come\
 , first served basis\, exceptions may be made to ensure diversity and equi
 ty\, which may increase the time before your registration is confirmed.\n\
 nApplications will close on **18/05/2026** or as soon as the places will b
 e filled up. Cancellation after **18/05/2026** will not be reimbursed. \n\
 nYou will be informed by email of your registration confirmation. Upon rec
 eption of the confirmation email\, participants will be asked to confirm a
 ttendance by paying the fees within 5 days.\n\n# Venue and Time\nPlease no
 te that this 2-day course will be streamed over 4 half-days\, from 14:00 t
 o 17:30 CEST of the following dates:\n* 01 June 2026\n* 08 June 2026\n* 15
  June 2026\n* 22 June 2026\n\nMore information will be provided to the reg
 istered participants in due time. \n\n#  Additional information\nCoordinat
 ion: Diana Marek\, SIB Training group.\n\nAfter the course\, we will provi
 de with a Certificate of Attendance or a Certificate of Achievement recomm
 ending 0.50 ECTS credits (given a passed exam).\n\nYou are welcome to regi
 ster 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](h
 ttps://lists.sib.swiss/mailman/listinfo/courses).\n\nPlease note that part
 icipation in SIB courses is subject to our [general conditions](http://www
 .sib.swiss/training/terms-and-conditions).\n\nSIB abides by the [ELIXIR Co
 de of Conduct](https://elixir-europe.org/events/code-of-conduct). Particip
 ants of SIB courses are also required to abide by the same code.\n\nFor mo
 re information\, please contact [training@sib.swiss](mailto:training@sib.s
 wiss).
SUMMARY:Introduction to Machine Learning with Python
URL;VALUE=URI:https://www.sib.swiss/training/course/20260601_INMLP
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