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SUMMARY:Τraining modules for SMEs - Module 9 "Applying Machine Learning t
 o Solve Real-world Challenges"
DTSTART;VALUE=DATE-TIME:20250901T090000Z
DTEND;VALUE=DATE-TIME:20250901T110000Z
DTSTAMP;VALUE=DATE-TIME:20260311T133600Z
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: TBA\n\nLocation: Online via Zoom\n\nPresentation Langu
 ages: Greek\n\nInstructor: Dr. Nikolaos Bakas\, Panagiota Gyftou\n\nDescri
 ption:\nThis interactive seminar is designed for professionals from SMEs w
 ho are ready to move from theory to practice. Each participant will select
  a topic of interest and analyze a dataset using AI/ML techniques\, train 
 machine learning models\, or utilize LLMs powered by Retrieval-Augmented G
 eneration (RAG). The seminar focuses on refining the use case through feed
 back from instructors and peers\, while it is dedicated to hands-on develo
 pment and presentation of results. This seminar emphasizes real-world appl
 ication\, peer learning\, and guided expert support to help participants t
 urn concepts into pilot outcomes.\n\nTarget Audience:\nProfessionals from 
 Small and Medium-sized Enterprises (SMEs) with foundational knowledge of A
 I/ML\, interested in applying these techniques to practical problems withi
 n their organization. Ideal for data analysts\, software developers\, tech
 nical leads\, and innovation managers.\n\nLearning Objectives:\n\n\n	\n	Tr
 anslate business needs into AI/ML project objectives.\n	\n	\n	Receive expe
 rt and peer feedback to refine project scope.\n	\n	\n	Apply AI/ML techniqu
 es using Python or LLMs frameworks.\n	\n	\n	Evaluate outcomes and present 
 findings or a prototype.\n	 \n	\n\n\nPrerequisites:\n\n\n	\n	Basic unders
 tanding of AI/ML concepts (e.g.\, supervised/unsupervised learning\, embed
 dings).\n	\n	\n	Familiarity with Python programming and relevant libraries
  (e.g.\, scikit-learn\, numpy\, pandas\, transformers).\n	\n	\n	Prior part
 icipation in foundational seminars (e.g.\, ensemble methods\, unsupervised
  learning\, rag) is strongly recommended.\n	 \n	\n\n\nIndicative Contents
 :\n\n\n	Welcome & Overview\n	Review of the material\n	Participant Use Case
 s\n	Instructor & Peer Feedback\n	Guidance on tools and implementation stra
 tegies\n	Individual Work Session: Planning & Setup\n	Hands-on Project Work
 \n	Troubleshooting & Support\n	Final Presentations & Peer Review\n	Discuss
 ion: Lessons Learned & Next Steps\n\n\nThe project is co-funded by the Eur
 opean Union. Views and opinions expressed are however those of the author(
 s) only and do not necessarily reflect those of the European Union or the 
 European Commission. Neither the European Union nor the granting authority
  can be held responsible for them. \n\nhttps://events.grnet.gr/event/177/
LOCATION:
URL:https://events.grnet.gr/event/177/
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