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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20241226T055607Z
UID:A19C4EA7-6BBC-4A5B-AD84-FE05539080B3
DTSTART;TZID=Asia/Kolkata:20241209T100000
DTEND;TZID=Asia/Kolkata:20241213T160000
DESCRIPTION:Introduction to Machine Learning and Mathematical Foundations\n
 \nLinear Algebra:\n\n-\nVectors\, Matrices\, and Tensors\n\n-\nEigenvalues
 \, Eigenvectors\, and Matrix Factorization\n\n-\nApplications of Linear Al
 gebra in Machine Learning\n\n-\nBasic implementation of matrix operations 
 in machine learning algorithms\n\nProbability Theory and Statistical Conce
 pts:\n\n-\nIntroduction to Probability Theory (Random Variables\, Distribu
 tions)\n\n-\nStatistical Measures (Mean\, Variance\, Covariance\, Correlat
 ion)\n\n-\nBayes’ Theorem and its importance in ML\n\nLinear and Logisti
 c Regression:\n\n-\nMathematics of Linear Regression (Least Squares\, Grad
 ient Descent)\n\n-\nIntroduction to Logistic Regression for Binary Classif
 ication\n\n[IEEE Workshop on Mathematics-driven Machine Learning]\n\nSpeak
 er(s): Dr. Pratik Barot\, Dr. Hiten Kanani\, Dr. Parita Shah\, Dr. Krunal 
 Kachhia\, Dr. Mrugendrasinh Rahevar\, Dr. Brajeshkumar Jha\, Dr. Safvan Va
 hora\, Dr. Tathagatha Bandyopadhyay\, Dr. Ojas Shriniwas\, Prof. Manoj Sah
 ni\n\nRoom: Auditorium Hall\, 3rd Floor\, Bldg: Biotechnology / Microbiolo
 gy Department\, LDRP Institute of Technology &amp; Research Campus\, Near KH-5
  Circle\, Gandhinagar\, Gujarat\, India\, 382016
LOCATION:Room: Auditorium Hall\, 3rd Floor\, Bldg: Biotechnology / Microbio
 logy Department\, LDRP Institute of Technology &amp; Research Campus\, Near KH
 -5 Circle\, Gandhinagar\, Gujarat\, India\, 382016
ORGANIZER:ashish_ce@ldrp.ac.in
SEQUENCE:21
SUMMARY:Workshop on Mathematics-Driven Machine Learning: Concept\, Techniqu
 es\, and Application
URL;VALUE=URI:https://events.vtools.ieee.org/m/445845
X-ALT-DESC:Description: &lt;br /&gt;&lt;div style=&quot;display: flex\; align-items: flex
 -start\;&quot;&gt;\n&lt;div style=&quot;flex: 3\; margin-right: 20px\;&quot;&gt;\n&lt;p dir=&quot;ltr&quot;&gt;Int
 roduction to Machine Learning and Mathematical Foundations&lt;/p&gt;\n&lt;p dir=&quot;lt
 r&quot;&gt;Linear Algebra:&lt;/p&gt;\n&lt;ul&gt;\n&lt;li dir=&quot;ltr&quot; aria-level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; 
 role=&quot;presentation&quot;&gt;Vectors\, Matrices\, and Tensors&lt;/p&gt;\n&lt;/li&gt;\n&lt;li dir=&quot;
 ltr&quot; aria-level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;Eigenvalues\, Eigen
 vectors\, and Matrix Factorization&lt;/p&gt;\n&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; aria-level=&quot;1
 &quot;&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;Applications of Linear Algebra in Mac
 hine Learning&lt;/p&gt;\n&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; aria-level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; role
 =&quot;presentation&quot;&gt;Basic implementation of matrix operations in machine learn
 ing algorithms&lt;/p&gt;\n&lt;/li&gt;\n&lt;/ul&gt;\n&lt;p dir=&quot;ltr&quot;&gt;Probability Theory and Stat
 istical Concepts:&lt;/p&gt;\n&lt;ul&gt;\n&lt;li dir=&quot;ltr&quot; aria-level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; r
 ole=&quot;presentation&quot;&gt;Introduction to Probability Theory (Random Variables\, 
 Distributions)&lt;/p&gt;\n&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; aria-level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; rol
 e=&quot;presentation&quot;&gt;Statistical Measures (Mean\, Variance\, Covariance\, Corr
 elation)&lt;/p&gt;\n&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; aria-level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;pre
 sentation&quot;&gt;Bayes&amp;rsquo\; Theorem and its importance in ML&lt;/p&gt;\n&lt;/li&gt;\n&lt;/ul
 &gt;\n&lt;p dir=&quot;ltr&quot;&gt;Linear and Logistic Regression:&lt;/p&gt;\n&lt;ul&gt;\n&lt;li dir=&quot;ltr&quot; a
 ria-level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;Mathematics of Linear Reg
 ression (Least Squares\, Gradient Descent)&lt;/p&gt;\n&lt;/li&gt;\n&lt;li dir=&quot;ltr&quot; aria-
 level=&quot;1&quot;&gt;\n&lt;p dir=&quot;ltr&quot; role=&quot;presentation&quot;&gt;Introduction to Logistic Regr
 ession for Binary Classification&lt;/p&gt;\n&lt;/li&gt;\n&lt;/ul&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;/div&gt;
 \n&lt;div style=&quot;flex: 2\;&quot;&gt;&lt;img style=&quot;max-width: 100%\; height: 600px\;&quot; sr
 c=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/f50d1b44-c862-40
 97-aa7f-272b9327d7e1&quot; alt=&quot;IEEE Workshop on Mathematics-driven Machine Lea
 rning&quot; width=&quot;423&quot;&gt;&lt;/div&gt;\n&lt;/div&gt;
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