BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20251009T100250Z
UID:56E9058F-14C9-42C9-BF17-A3B79D128A52
DTSTART;TZID=Asia/Kolkata:20250919T093000
DTEND;TZID=Asia/Kolkata:20250920T160000
DESCRIPTION:The workshop aims to provide our students with industry-oriente
 d skills that are highly sought after in the software sector. Participants
  will gain exposure to real-world datasets such as 20 Newsgroups (NLP)\, M
 NIST/CIFAR-10 (Image Processing)\, and UNSW-NB15 (Cybersecurity)\, and wil
 l acquire practical knowledge in AI frameworks like TensorFlow\, PyTorch h
 ugging Face\, and Scikit-learn. Through guided sessions\, students will de
 velop the ability to implement machine learning models\, design neural net
 works\, and apply reinforcement learning to solve real-time engineering pr
 oblems.\n\nSpeaker(s): Dr Kishore\, Dr M Srinivas\n\nAgenda: \nOrganized b
 y: Department of Computer Science and Engineering\, BVCITS\nDates: 19th &amp; 
 20th September 2025\nVenue: [Ac Seminar hall \,1st year block]\n----------
 -----------------------------------------------------\n\n📅 Day 1: 19th 
 September 2025\n\nTime	Session Details\n09:30 – 10:45 AM	Inauguration Ce
 remony: Inviting dignitaries on the dais\, followed by an address from the
  Principal.\n📢 Expert Talk by Dr. Kishore Babu on Machine Learning (Sup
 ervised &amp; Unsupervised Learning).\n10:45 – 11:45 AM	Hands-on Session: Su
 pervised and Unsupervised Learning\n11:45 – 01:30 PM	Workshop Activity: 
 Solving Real-Time Problems in Machine Learning\n01:30 – 02:30 PM	🍴 Lu
 nch Break\n02:30 – 03:50 PM	Hands-on Session: Real-Time Problem Solving 
 in Deep Learning\n03:50 – 04:00 PM	Closing Remarks &amp; Day 1 Conclusion\n-
 --------------------------------------------------------------\n\n📅 Day
  2: 20th September 2025\n\nTime	Session Details\n09:30 – 11:00 AM	Techni
 cal Session: Symbolic &amp; Rule-Based NLP\n11:00 – 12:00 PM	Technical Sessi
 on: Statistical NLP (HMMs\, N-gram Models)\n12:00 – 01:00 PM	🍴 Lunch 
 Break\n01:00 – 02:00 PM	Deep Dive: Neural Word Embeddings &amp; RNN (LSTM Mo
 dels)\n02:00 – 03:00 PM	Technical Session: Transformer Architecture (Sel
 f-Attention Mechanism)\n03:00 – 04:00 PM	Concluding Session: Large Pre-T
 rained Multimodal Models\n------------------------------------------------
 ---------------\n\nRoom: AC Seminar Hall\, Bldg: I Year Block\, BVCITS(A)\
 , Batlapalem\, Amalapuram\, Andhra Pradesh\, India\, 533221
LOCATION:Room: AC Seminar Hall\, Bldg: I Year Block\, BVCITS(A)\, Batlapale
 m\, Amalapuram\, Andhra Pradesh\, India\, 533221
ORGANIZER:srinivas.katikireddy@gmail.com
SEQUENCE:2
SUMMARY:A Two Day Workshop on &quot;Artificial Intelligence and Machine Learning
  Tools&quot;
URL;VALUE=URI:https://events.vtools.ieee.org/m/500207
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;
 font-size: 11.0pt\; line-height: 115%\; font-family: &#39;Times New Roman&#39;\,se
 rif\; mso-fareast-font-family: &#39;MS Mincho&#39;\; mso-fareast-theme-font: minor
 -fareast\; mso-ansi-language: EN-US\; mso-fareast-language: EN-US\; mso-bi
 di-language: AR-SA\;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/m
 edia/display/dadaeaac-c8f4-4d9f-bff2-55a84cec43b1&quot; width=&quot;486&quot; height=&quot;322
 &quot;&gt; &lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/28f3d4
 08-a341-4697-802b-1f5b4f884604&quot; width=&quot;240&quot; height=&quot;321&quot;&gt;&lt;img src=&quot;https:/
 /events.vtools.ieee.org/vtools_ui/media/display/5d4f4a5e-7915-4554-b186-c0
 45a30e3a55&quot; width=&quot;244&quot; height=&quot;321&quot;&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;img style=&quot;fl
 oat: left\;&quot; src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/8
 a13c010-9505-4604-bec8-a556f8496a75&quot; width=&quot;389&quot; height=&quot;294&quot;&gt;&lt;img src=&quot;ht
 tps://events.vtools.ieee.org/vtools_ui/media/display/7aa8307c-ab05-419a-83
 38-87e85000bdf6&quot; width=&quot;390&quot; height=&quot;294&quot;&gt;&lt;/p&gt;\n&lt;p&gt;&lt;img src=&quot;https://event
 s.vtools.ieee.org/vtools_ui/media/display/43896f74-3c06-4e91-b3e7-2a8d20d6
 39cd&quot; width=&quot;340&quot; height=&quot;257&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vt
 ools_ui/media/display/390d0f8a-2228-4bf9-bf8b-fb43ed9a5154&quot; width=&quot;306&quot; he
 ight=&quot;255&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/displa
 y/ad453782-2a93-4c67-8725-125bf994af0f&quot; width=&quot;340&quot; height=&quot;256&quot;&gt;&lt;/p&gt;\n&lt;p&gt;
 &lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 11.0pt\; line-height: 115%\; font-fam
 ily: &#39;Times New Roman&#39;\,serif\; mso-fareast-font-family: &#39;MS Mincho&#39;\; mso
 -fareast-theme-font: minor-fareast\; mso-ansi-language: EN-US\; mso-fareas
 t-language: EN-US\; mso-bidi-language: AR-SA\;&quot;&gt;The workshop aims to provi
 de our students with industry-oriented skills that are highly sought after
  in the software sector. Participants will gain exposure to real-world dat
 asets such as&lt;strong&gt; 20 Newsgroups (NLP)\, MNIST/CIFAR-10 (Image Processi
 ng)\, and UNSW-NB15 (Cybersecurity)&lt;/strong&gt;\, and will acquire practical 
 knowledge in &lt;strong&gt;A&lt;/strong&gt;&lt;/span&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;T
 imes New Roman&#39;\, serif\; font-size: 14.6667px\;&quot;&gt;&lt;strong&gt;I&lt;/strong&gt; frame
 works like TensorFlow\, PyTorch&amp;nbsp\; &lt;/span&gt;&lt;span style=&quot;font-family: &#39;T
 imes New Roman&#39;\, serif\; font-size: 14.6667px\;&quot;&gt;h&lt;/span&gt;&lt;/strong&gt;&lt;span s
 tyle=&quot;font-family: &#39;Times New Roman&#39;\, serif\; font-size: 11pt\;&quot;&gt;&lt;strong&gt;
 ugging Face\, and Scikit-learn.&lt;/strong&gt; Through guided sessions\, student
 s will develop the ability to implement &lt;strong&gt;machine learning models\, 
 design neural networks\, and apply reinforcement learning to solve real-ti
 me engineering problems.&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p d
 ata-start=&quot;224&quot; data-end=&quot;387&quot;&gt;&lt;strong data-start=&quot;224&quot; data-end=&quot;241&quot;&gt;Org
 anized by:&lt;/strong&gt; Department of Computer Science and Engineering\, BVCIT
 S&lt;br data-start=&quot;296&quot; data-end=&quot;299&quot;&gt;&lt;strong data-start=&quot;299&quot; data-end=&quot;30
 9&quot;&gt;Dates:&lt;/strong&gt; 19th &amp;amp\; 20th September 2025&lt;br data-start=&quot;336&quot; dat
 a-end=&quot;339&quot;&gt;&lt;strong data-start=&quot;339&quot; data-end=&quot;349&quot;&gt;Venue:&lt;/strong&gt; [Ac Se
 minar hall \,1st year block]&lt;/p&gt;\n&lt;hr data-start=&quot;389&quot; data-end=&quot;392&quot;&gt;\n&lt;h
 3 data-start=&quot;394&quot; data-end=&quot;431&quot;&gt;📅 &lt;strong data-start=&quot;401&quot; data-end=&quot;
 431&quot;&gt;Day 1: 19th September 2025&lt;/strong&gt;&lt;/h3&gt;\n&lt;div class=&quot;_tableContainer
 _1rjym_1&quot;&gt;\n&lt;div class=&quot;group _tableWrapper_1rjym_13 flex w-fit flex-col-r
 everse&quot; tabindex=&quot;-1&quot;&gt;\n&lt;table class=&quot;w-fit min-w-(--thread-content-width)
 &quot; data-start=&quot;433&quot; data-end=&quot;1518&quot;&gt;\n&lt;thead data-start=&quot;433&quot; data-end=&quot;553
 &quot;&gt;\n&lt;tr data-start=&quot;433&quot; data-end=&quot;553&quot;&gt;\n&lt;th data-start=&quot;433&quot; data-end=&quot;4
 53&quot; data-col-size=&quot;sm&quot;&gt;Time&lt;/th&gt;\n&lt;th data-start=&quot;453&quot; data-end=&quot;553&quot; data
 -col-size=&quot;xl&quot;&gt;Session Details&lt;/th&gt;\n&lt;/tr&gt;\n&lt;/thead&gt;\n&lt;tbody data-start=&quot;6
 75&quot; data-end=&quot;1518&quot;&gt;\n&lt;tr data-start=&quot;675&quot; data-end=&quot;906&quot;&gt;\n&lt;td data-start
 =&quot;675&quot; data-end=&quot;698&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;677&quot; data-end
 =&quot;697&quot;&gt;09:30 &amp;ndash\; 10:45 AM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;698&quot; data-en
 d=&quot;906&quot; data-col-size=&quot;xl&quot;&gt;Inauguration Ceremony: Inviting dignitaries on 
 the dais\, followed by an address from the Principal. &lt;br&gt;📢 Expert Talk
  by &lt;strong data-start=&quot;822&quot; data-end=&quot;842&quot;&gt;Dr. Kishore Babu&lt;/strong&gt; on &lt;
 strong data-start=&quot;846&quot; data-end=&quot;866&quot;&gt;Machine Learning&lt;/strong&gt; (Supervis
 ed &amp;amp\; Unsupervised Learning).&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;907&quot; data-e
 nd=&quot;1029&quot;&gt;\n&lt;td data-start=&quot;907&quot; data-end=&quot;930&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong
  data-start=&quot;909&quot; data-end=&quot;929&quot;&gt;10:45 &amp;ndash\; 11:45 AM&lt;/strong&gt;&lt;/td&gt;\n&lt;t
 d data-start=&quot;930&quot; data-end=&quot;1029&quot; data-col-size=&quot;xl&quot;&gt;Hands-on Session: &lt;s
 trong data-start=&quot;950&quot; data-end=&quot;990&quot;&gt;Supervised and Unsupervised Learning
 &lt;/strong&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;1030&quot; data-end=&quot;1151&quot;&gt;\n&lt;td data-st
 art=&quot;1030&quot; data-end=&quot;1053&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;1032&quot; da
 ta-end=&quot;1052&quot;&gt;11:45 &amp;ndash\; 01:30 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;1053&quot;
  data-end=&quot;1151&quot; data-col-size=&quot;xl&quot;&gt;Workshop Activity: &lt;strong data-start=
 &quot;1074&quot; data-end=&quot;1124&quot;&gt;Solving Real-Time Problems in Machine Learning&lt;/str
 ong&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;1152&quot; data-end=&quot;1274&quot;&gt;\n&lt;td data-start=&quot;
 1152&quot; data-end=&quot;1175&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;1154&quot; data-en
 d=&quot;1174&quot;&gt;01:30 &amp;ndash\; 02:30 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;1175&quot; data
 -end=&quot;1274&quot; data-col-size=&quot;xl&quot;&gt;🍴 &lt;strong data-start=&quot;1180&quot; data-end=&quot;11
 95&quot;&gt;Lunch Break&lt;/strong&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;1275&quot; data-end=&quot;1396
 &quot;&gt;\n&lt;td data-start=&quot;1275&quot; data-end=&quot;1298&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-
 start=&quot;1277&quot; data-end=&quot;1297&quot;&gt;02:30 &amp;ndash\; 03:50 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td da
 ta-start=&quot;1298&quot; data-end=&quot;1396&quot; data-col-size=&quot;xl&quot;&gt;Hands-on Session: &lt;stro
 ng data-start=&quot;1318&quot; data-end=&quot;1364&quot;&gt;Real-Time Problem Solving in Deep Lea
 rning&lt;/strong&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;1397&quot; data-end=&quot;1518&quot;&gt;\n&lt;td da
 ta-start=&quot;1397&quot; data-end=&quot;1420&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;139
 9&quot; data-end=&quot;1419&quot;&gt;03:50 &amp;ndash\; 04:00 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;
 1420&quot; data-end=&quot;1518&quot; data-col-size=&quot;xl&quot;&gt;Closing Remarks &amp;amp\; &lt;strong da
 ta-start=&quot;1440&quot; data-end=&quot;1460&quot;&gt;Day 1 Conclusion&lt;/strong&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/t
 body&gt;\n&lt;/table&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;hr data-start=&quot;1520&quot; data-end=&quot;1523&quot;&gt;\n&lt;
 h3 data-start=&quot;1525&quot; data-end=&quot;1562&quot;&gt;📅 &lt;strong data-start=&quot;1532&quot; data-e
 nd=&quot;1562&quot;&gt;Day 2: 20th September 2025&lt;/strong&gt;&lt;/h3&gt;\n&lt;div class=&quot;_tableCont
 ainer_1rjym_1&quot;&gt;\n&lt;div class=&quot;group _tableWrapper_1rjym_13 flex w-fit flex-
 col-reverse&quot; tabindex=&quot;-1&quot;&gt;\n&lt;table class=&quot;w-fit min-w-(--thread-content-w
 idth)&quot; data-start=&quot;1564&quot; data-end=&quot;2537&quot;&gt;\n&lt;thead data-start=&quot;1564&quot; data-e
 nd=&quot;1684&quot;&gt;\n&lt;tr data-start=&quot;1564&quot; data-end=&quot;1684&quot;&gt;\n&lt;th data-start=&quot;1564&quot; 
 data-end=&quot;1584&quot; data-col-size=&quot;sm&quot;&gt;Time&lt;/th&gt;\n&lt;th data-start=&quot;1584&quot; data-e
 nd=&quot;1684&quot; data-col-size=&quot;md&quot;&gt;Session Details&lt;/th&gt;\n&lt;/tr&gt;\n&lt;/thead&gt;\n&lt;tbody
  data-start=&quot;1806&quot; data-end=&quot;2537&quot;&gt;\n&lt;tr data-start=&quot;1806&quot; data-end=&quot;1928&quot;
 &gt;\n&lt;td data-start=&quot;1806&quot; data-end=&quot;1829&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-s
 tart=&quot;1808&quot; data-end=&quot;1828&quot;&gt;09:30 &amp;ndash\; 11:00 AM&lt;/strong&gt;&lt;/td&gt;\n&lt;td dat
 a-start=&quot;1829&quot; data-end=&quot;1928&quot; data-col-size=&quot;md&quot;&gt;Technical Session: &lt;stro
 ng data-start=&quot;1850&quot; data-end=&quot;1879&quot;&gt;Symbolic &amp;amp\; Rule-Based NLP&lt;/stron
 g&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;1929&quot; data-end=&quot;2050&quot;&gt;\n&lt;td data-start=&quot;19
 29&quot; data-end=&quot;1952&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;1931&quot; data-end=
 &quot;1951&quot;&gt;11:00 &amp;ndash\; 12:00 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;1952&quot; data-e
 nd=&quot;2050&quot; data-col-size=&quot;md&quot;&gt;Technical Session: &lt;strong data-start=&quot;1973&quot; 
 data-end=&quot;1992&quot;&gt;Statistical NLP&lt;/strong&gt; (HMMs\, N-gram Models)&lt;/td&gt;\n&lt;/tr
 &gt;\n&lt;tr data-start=&quot;2051&quot; data-end=&quot;2173&quot;&gt;\n&lt;td data-start=&quot;2051&quot; data-end=
 &quot;2074&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;2053&quot; data-end=&quot;2073&quot;&gt;12:00 
 &amp;ndash\; 01:00 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;2074&quot; data-end=&quot;2173&quot; dat
 a-col-size=&quot;md&quot;&gt;🍴 &lt;strong data-start=&quot;2079&quot; data-end=&quot;2094&quot;&gt;Lunch Break
 &lt;/strong&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;2174&quot; data-end=&quot;2294&quot;&gt;\n&lt;td data-st
 art=&quot;2174&quot; data-end=&quot;2197&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;2176&quot; da
 ta-end=&quot;2196&quot;&gt;01:00 &amp;ndash\; 02:00 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;2197&quot;
  data-end=&quot;2294&quot; data-col-size=&quot;md&quot;&gt;Deep Dive: &lt;strong data-start=&quot;2210&quot; d
 ata-end=&quot;2256&quot;&gt;Neural Word Embeddings &amp;amp\; RNN (LSTM Models)&lt;/strong&gt;&lt;/t
 d&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;2295&quot; data-end=&quot;2416&quot;&gt;\n&lt;td data-start=&quot;2295&quot; d
 ata-end=&quot;2318&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;2297&quot; data-end=&quot;2317
 &quot;&gt;02:00 &amp;ndash\; 03:00 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;2318&quot; data-end=&quot;2
 416&quot; data-col-size=&quot;md&quot;&gt;Technical Session: &lt;strong data-start=&quot;2339&quot; data-
 end=&quot;2367&quot;&gt;Transformer Architecture&lt;/strong&gt; (Self-Attention Mechanism)&lt;/t
 d&gt;\n&lt;/tr&gt;\n&lt;tr data-start=&quot;2417&quot; data-end=&quot;2537&quot;&gt;\n&lt;td data-start=&quot;2417&quot; d
 ata-end=&quot;2440&quot; data-col-size=&quot;sm&quot;&gt;&lt;strong data-start=&quot;2419&quot; data-end=&quot;2439
 &quot;&gt;03:00 &amp;ndash\; 04:00 PM&lt;/strong&gt;&lt;/td&gt;\n&lt;td data-start=&quot;2440&quot; data-end=&quot;2
 537&quot; data-col-size=&quot;md&quot;&gt;Concluding Session: &lt;strong data-start=&quot;2462&quot; data
 -end=&quot;2501&quot;&gt;Large Pre-Trained Multimodal Models&lt;/strong&gt;&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tb
 ody&gt;\n&lt;/table&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;hr data-start=&quot;2539&quot; data-end=&quot;2542&quot;&gt;\n&lt;p
  data-start=&quot;2544&quot; data-end=&quot;2637&quot; data-is-last-node=&quot;&quot; data-is-only-node=
 &quot;&quot;&gt;&amp;nbsp\;&lt;/p&gt;
END:VEVENT
END:VCALENDAR