BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Τraining modules for SMEs - Module 9 "Applying Machine Learning t
 o Solve Real-world Challenges"
DTSTART;VALUE=DATE-TIME:20250630T090000Z
DTEND;VALUE=DATE-TIME:20250630T110000Z
DTSTAMP;VALUE=DATE-TIME:20261008T163910Z
UID:indico-event-177@events.grnet.gr
DESCRIPTION:\n\n\n\nGRNET announces\, in the context of SmartAttica EDIH (
 European Digital Innovation Hub)\, the 9th Module of Τraining modules for
  SMEs  with the subject "Applying Machine Learning to Solve Real-world Cha
 llenges".  \n\nDate: June 30th\, 2025\n\nLocation: Online via Zoom\n\nPr
 esentation Languages: Greek\n\nInstructor: Dr. Nikolaos Bakas\, Panagiota 
 Gyftou\n\nDescription:\nThis interactive seminar is designed for professio
 nals from SMEs who are ready to move from theory to practice. Each partici
 pant will select a topic of interest and analyze a dataset using AI/ML tec
 hniques\, train machine learning models\, or utilize LLMs powered by Retri
 eval-Augmented Generation (RAG). The seminar focuses on refining the use c
 ase through feedback from instructors and peers\, while it is dedicated to
  hands-on development and presentation of results. This seminar emphasizes
  real-world application\, peer learning\, and guided expert support to hel
 p participants turn concepts into pilot outcomes.\n\nTarget Audience:\nPro
 fessionals from Small and Medium-sized Enterprises (SMEs) with foundationa
 l knowledge of AI/ML\, interested in applying these techniques to practica
 l problems within their organization. Ideal for data analysts\, software d
 evelopers\, technical leads\, and innovation managers.\n\nLearning Objecti
 ves:\n\n\n	\n	Translate business needs into AI/ML project objectives.\n	\n
 	\n	Receive expert and peer feedback to refine project scope.\n	\n	\n	Appl
 y AI/ML techniques using Python or LLMs frameworks.\n	\n	\n	Evaluate outco
 mes and present findings or a prototype.\n	 \n	\n\n\nPrerequisites:\n\n\n
 	\n	Basic understanding of AI/ML concepts (e.g.\, supervised/unsupervised 
 learning\, embeddings).\n	\n	\n	Familiarity with Python programming and re
 levant libraries (e.g.\, scikit-learn\, numpy\, pandas\, transformers).\n	
 \n	\n	Prior participation in foundational seminars (e.g.\, ensemble method
 s\, unsupervised learning\, rag) is strongly recommended.\n	 \n	\n\n\nInd
 icative Contents:\n\n\n	Welcome & Overview\n	Review of the material\n	Part
 icipant Use Cases\n	Instructor & Peer Feedback\n	Guidance on tools and imp
 lementation strategies\n	Individual Work Session: Planning & Setup\n	Hands
 -on Project Work\n	Troubleshooting & Support\n	Final Presentations & Peer 
 Review\n	Discussion: Lessons Learned & Next Steps\n\n\nThe project is co-f
 unded by the European Union. Views and opinions expressed are however thos
 e of the author(s) only and do not necessarily reflect those of the Europe
 an Union or the European Commission. Neither the European Union nor the gr
 anting authority can be held responsible for them. \n\nhttps://events.grne
 t.gr/event/177/
LOCATION:
URL:https://events.grnet.gr/event/177/
END:VEVENT
END:VCALENDAR