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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
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BEGIN:VEVENT
DTSTAMP:20200924T114728Z
UID:3EF10D52-87AE-4D5F-A02D-DFD2C09B49DD
DTSTART;TZID=Asia/Kolkata:20200703T140000
DTEND;TZID=Asia/Kolkata:20200703T160000
DESCRIPTION:- At the very beginning of the presentation\, there was a recap
  on general classification from previous sessions where topics like Featur
 e Extraction\, Ensemble Learning\, cross-validation\, etc. in order to acq
 uire the best performance of a Machine Learning model.\n- Then\, the issue
 s like the presence of noise in data\, correlation among features\, curse 
 of dimensionality\, etc. were discussed\, how general classification techn
 iques become ineffective\, and how these issues degrade the model performa
 nce.\n- A common misconception on the difference between the terms ‘More
  data’ and ‘More features’ was cleared\, for further progress of the
  session.\n- Advantages of Dimensionality Reduction Techniques were discus
 sed and how it is useful in obtaining optimal number of features\, explain
 ed along with examples.\n- Two popular techniques for Dimensionality Reduc
 tion were discussed. They are:\n- PCA (Principal Component Analysis)\n- LD
 A (Linear Discriminant Analysis)\n\n- Each of the techniques was explained
  with step-by-step mathematical computations\, where topics like Linear Al
 gebra\, Matrices\, Eigenvalues and Eigenvectors\, Covariance of a Matrix\,
  etc. were covered\, along with numerical examples.\n- A use-case of Princ
 ipal Component Analysis was covered in a real-time application\, i.e. in E
 lectrical Activity of Heart by analyzing ECG Signal data. The Program of t
 he same\, was demonstrated on MATLAB.\n\nSpeaker(s): L.V. Rajani Kumari\, 
 \n\nAgenda: \nIn this session\, the resource person presented a brief intr
 oduction\, on what is dimensionality reduction\, problems with high dimens
 ional data\, and the need of Dimensionality Reduction Techniques in the Fe
 ature Extraction process. Some of the well-known techniques were keenly di
 scussed and demonstrated on MATLAB.\n\nHyderabad\, Andhra Pradesh\, India\
 , Virtual: https://events.vtools.ieee.org/m/240901
LOCATION:Hyderabad\, Andhra Pradesh\, India\, Virtual: https://events.vtool
 s.ieee.org/m/240901
ORGANIZER:Rajeshwari_I@ieee.org
SEQUENCE:1
SUMMARY:ML Webinar Series (7)-Dimensionality Reduction
URL;VALUE=URI:https://events.vtools.ieee.org/m/240901
X-ALT-DESC:Description: <br /><ul>\n<li>At the very beginning of the presen
 tation\, there was a recap on general classification from previous session
 s where topics like Feature Extraction\, Ensemble Learning\, cross-validat
 ion\, etc. in order to acquire the best performance of a Machine Learning 
 model.</li>\n<li>Then\, the issues like the presence of noise in data\, co
 rrelation among features\, curse of dimensionality\, etc. were discussed\,
  how general classification techniques become ineffective\, and how these 
 issues degrade the model performance.</li>\n<li>A common misconception on 
 the difference between the terms &lsquo\;More data&rsquo\; and &lsquo\;Mor
 e features&rsquo\; was cleared\, for further progress of the session.</li>
 \n<li>Advantages of Dimensionality Reduction Techniques were discussed and
  how it is useful in obtaining optimal number of features\, explained alon
 g with examples.</li>\n<li>Two popular techniques for Dimensionality Reduc
 tion were discussed. They are:\n<ul>\n<li>PCA (Principal Component Analysi
 s)</li>\n<li>LDA (Linear Discriminant Analysis)</li>\n</ul>\n</li>\n<li>Ea
 ch of the techniques was explained with step-by-step mathematical computat
 ions\, where topics like Linear Algebra\, Matrices\, Eigenvalues and Eigen
 vectors\, Covariance of a Matrix\, etc. were covered\, along with numerica
 l examples.</li>\n<li>A use-case of Principal Component Analysis was cover
 ed in a real-time application\, i.e. in Electrical Activity of Heart by an
 alyzing ECG Signal data. The Program of the same\, was demonstrated on MAT
 LAB.</li>\n</ul><br /><br />Agenda: <br /><p>In this session\, the resourc
 e person presented a brief introduction\, on what is dimensionality reduct
 ion\, problems with high dimensional data\, and the need of Dimensionality
  Reduction Techniques in the Feature Extraction process. Some of the well-
 known techniques were keenly discussed and demonstrated on MATLAB.</p>
END:VEVENT
END:VCALENDAR