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DTSTAMP:20241011T155906Z
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DTSTART;TZID=Europe/London:20241003T210000
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DESCRIPTION:The second session started with a follow-through of the concept
 s shared in the previous session. It was led by the same trainer\, startin
 g by greeting participants to make sure interaction is ensured.\n\nThe ses
 sion kicked off with a quick review of what had been covered during the pr
 evious camp\, then did a deep dive on more advanced AI concepts: object de
 tection\, instance segmentation\, and a comparison between different types
  of AI. Participants were introduced to generative AI\, machine learning\,
  natural language processing\, and computer vision\, including discussions
  on supervised and unsupervised learning\, and reinforcement learning.\n\n
 Subsequently\, it went to a practical session on data handling\, which can
  be summarized to include topics such as data cleaning\, curation\, featur
 e selection\, splitting and learning algorithms. He then presented a coupl
 e of clustering techniques and showed the trainer original and clustered d
 ata\, then proceeded with dimensionality reduction.\n\nThe session involve
 d questions by the participants\, and the trainer had also provided practi
 cal examples with the help of the Jupyter Notebook integrated into Visual 
 Studio Code. Examples ranged from algorithms on k-nearest neighbors (k-NN)
  to other functions used within machine learning models.\n\nA practical CS
 V workshop was then provided to the participants. The trainer performed so
 me hands-on machine learning techniques\, focusing on linear regression an
 d writing several related mathematical formulas and programming functions.
  He explained how to calculate cost functions\, regression metrics\, and r
 esiduals.\n\nThe participants were advised to use Anaconda because it is a
 n all-inclusive tool in terms of various libraries and software\, or to wo
 rk with Google Colab on the cloud. They will be working within Python usin
 g libraries like "numpy"\, "Pandas"\, "Matplotlib"\, and "Seaborn". The se
 ssion wrapped up with hands-on exercises left and further Q&A for remainin
 g questions.\n\nSpeaker(s): Ahmed Chtourou\n\nVirtual: https://events.vtoo
 ls.ieee.org/m/438849
LOCATION:Virtual: https://events.vtools.ieee.org/m/438849
ORGANIZER:ala.benameur@ieee.org
SEQUENCE:43
SUMMARY:IEEE Day Technical Activities\, AI Bootcamp #2
URL;VALUE=URI:https://events.vtools.ieee.org/m/438849
X-ALT-DESC:Description: <br /><p style="text-align: center\;">The second se
 ssion started with a follow-through of the concepts shared in the previous
  session. It was led by the same trainer\, starting by greeting participan
 ts to make sure interaction is ensured.</p>\n<p style="text-align: center\
 ;">The session kicked off with a quick review of what had been covered dur
 ing the previous camp\, then did a deep dive on more advanced AI concepts:
  object detection\, instance segmentation\, and a comparison between diffe
 rent types of AI. Participants were introduced to generative AI\, machine 
 learning\, natural language processing\, and computer vision\, including d
 iscussions on supervised and unsupervised learning\, and reinforcement lea
 rning.</p>\n<p style="text-align: center\;">Subsequently\, it went to a pr
 actical session on data handling\, which can be summarized to include topi
 cs such as data cleaning\, curation\, feature selection\, splitting and le
 arning algorithms. He then presented a couple of clustering techniques and
  showed the trainer original and clustered data\, then proceeded with dime
 nsionality reduction.</p>\n<p style="text-align: center\;">The session inv
 olved questions by the participants\, and the trainer had also provided pr
 actical examples with the help of the Jupyter Notebook integrated into Vis
 ual Studio Code. Examples ranged from algorithms on k-nearest neighbors (k
 -NN) to other functions used within machine learning models.</p>\n<p style
 ="text-align: center\;">A practical CSV workshop was then provided to the 
 participants. The trainer performed some hands-on machine learning techniq
 ues\, focusing on linear regression and writing several related mathematic
 al formulas and programming functions. He explained how to calculate cost 
 functions\, regression metrics\, and residuals.</p>\n<p style="text-align:
  center\;">The participants were advised to use Anaconda because it is an 
 all-inclusive tool in terms of various libraries and software\, or to work
  with Google Colab on the cloud. They will be working within Python using 
 libraries like "numpy"\, "Pandas"\, "Matplotlib"\, and "Seaborn". The sess
 ion wrapped up with hands-on exercises left and further Q&amp\;A for remai
 ning questions.</p>
END:VEVENT
END:VCALENDAR