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DTSTAMP:20260730T085323Z
UID:f34c1c0b-53d9-4b06-8830-77ef1ff36d2c
DTSTART:20260803T123000Z
DTEND:20260804T165000Z
DESCRIPTION:Speakers: Richa Sharma (University of Puerto Rico (US))\, Valer
 iia Lukashenko (University of Zurich (CH))\, Alexander Moreno Briceño (Un
 iversidad Antonio Nariño)\, Andres Rios-Tascon (Princeton University)\n\n
       \nWe are very excited to announce a training event on Machine Lea
 rning/Deep Learning organised through the HEP Software Foundation and IRIS
 -HEP \n \nAll sessions will take place in the US Eastern Time zone.\nPle
 ase contact the organizers (email us) in case of any questions.\nWhat exac
 tly will I learn?\nThe main objective of this course are\n\n\nTo introduce
  you to the basics of Machine learning with examples.\n\n\nTo develop a se
 nse of statistics/data science algorithms that goes under the hood of a ML
  model.\n\n\nExplain the terminology of machine learning.\n\n\nIntroducing
  you to some Python frameworks to start building your first Machine.\n\n\n
 Getting familiarize with basic ML models that are although very common but
  can serve as a basic starting point.\n\n\nGetting you prepared to learn o
 n your own once this course is over.\n\n\nAre there any prerequisites?\nYe
 s! \nHard Prerequisites\nParticipants should have basic experience with P
 ython\, including writing or modifying simple scripts\, using functions an
 d packages\, and understanding simple error messages. You should also be a
 ble to run and edit a Jupyter Notebook or Google Colab notebook\, use basi
 c terminal commands\, and install Python packages when needed. A working P
 ython environment is required\; Google Colab will be supported\, although 
 a local conda\, mamba\, or pip environment is encouraged for the exercises
 .\nSoft Prerequisites\nBasic familiarity with functions\, vectors and matr
 ices\, derivatives\, probability\, and statistics will be helpful. Prior e
 xperience with NumPy\, Pandas\, plotting\, Scikit-Learn\, PyTorch\, Git\, 
 machine learning\, or HEP data analysis is useful but not required. The co
 urse is intended for participants with different levels of ML experience\,
  but it will not provide an introduction to Python programming.\nWho is su
 pporting this?\nThis event is supported by CERN and U.S. National Science 
 Foundation Cooperative Agreement PHY-2323298 (IRIS-HEP).\nWho is teaching 
 this thing?\nThis is a hands-on training and consists of live lectures by 
 the instructors via Zoom.  Along with this\, there are mentors who will g
 ive individual attention and to debug assistance to participants via chat 
 tools.  The people filling these roles are listed below.  \nInstructors
 : \n\n\n\nArghya Chattopadhyay (University of Puerto Rico Mayaguez)\n\n\n
 \nMentors (on Slack): \n\nQuinn Campagna (University of Mississippi)\nMat
 eo E Lisondo (University of Puerto Rico Mayaguez)\nKaran Singh \nAashirva
 d (Manipal Academy of Higher Education)\nJuvenal Bassa (University of Puer
 to Rico Mayaguez)\n\n\nhttps://indico.cern.ch/event/1706524/
LOCATION:Virtual
SUMMARY:Machine Learning Training - Intermediate Level  (Virtual)
URL;VALUE=URI:https://indico.cern.ch/event/1706524/
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