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picture1_Matrix Calculus Pdf 171181 | Mit18 S096iap22 Lec1new


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File: Matrix Calculus Pdf 171181 | Mit18 S096iap22 Lec1new
lecture slides for matrix calculus winter 2022 alan edelman steven johnson 11am 1pm mwf 1 lectures january 10 12 14 19 21 24 26 28 11am 1pm virtual short break ...

icon picture PDF Filetype PDF | Posted on 26 Jan 2023 | 2 years ago
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                   Lecture Slides for
                      Matrix Calculus
                           Winter 2022
             Alan Edelman/Steven Johnson
                            11am-1pm MWF
                                                                                 1
      ● Lectures: January 10,12,14,19,21,24,26,28
      ● 11am-1pm virtual, short break around noon
      ● Two Homeworks: Released Wednesday due
          following Wednesday on canvas, 11:59pm due
      ● 3 Credits
      ● Linear Algebra such as 18.06 assumed
      Some demos and hw may use             (no experience assumed, though most 
      LinAlg classes at MIT use a little Julia already)
                                                                                  2
   Where does matrix calculus fit in?
      ● MIT 18.01: Scalar or Single Variable Calculus
      ● MIT 18.02: Vector or Multivariable Calculus
     Perhaps an ideal world might go Scalar, Vector, Matrix, Higher Dimensional Arrays…
           (0 dimensional, 1 dimensional, 2 dimensional…)
           (e.g. size(scalar)=[], size(vector)=[n], size(matrix)=[m,n],...)
           (some programming language do not implement this fully)
     Why now?
       ●   In the last decade or two, the role of linear algebra has taken on larger importance in lots of areas
           including Machine Learning, Statistics, Engineering, etc.
       ●   Warning: googling Matrix Calculus may only give a small view of the full range of the mathematics that we
                                                            2
           hope to cover example what is the derivative of X  when X is a square matrix?  Should it be 2X? (It’s not). 
                         -1   -2
           What about X ? -X ? (Not quite).
                                                                                                                   3
  Applications: Machine Learning
  buzzwords: parameter optimization
  stochastic gradient descent, autodiff,
  backpropagation
                           © University of Tubingen. All rights reserved. This content is excluded from our Creative 
                           Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use.
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                                                                  This content is excluded from our Creative 
                                                                  Commons license. For more information, see 
                                                                  https://ocw.mit.edu/help/faq-fair-use.
  © Medium (medium.com). All rights reserved. This content is 
  excluded from our Creative Commons license. For more 
  information, see https://ocw.mit.edu/help/faq-fair-use.
                                                                                      4
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                             Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use.
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...Lecture slides for matrix calculus winter alan edelman steven johnson am pm mwf lectures january virtual short break around noon two homeworks released wednesday due following on canvas credits linear algebra such as assumed some demos and hw may use no experience though most linalg classes at mit a little julia already where does fit in scalar or single variable vector multivariable perhaps an ideal world might go higher dimensional arrays e g size programming language do not implement this fully why now the last decade role of has taken larger importance lots areas including machine learning statistics engineering etc warning googling only give small view full range mathematics that we hope to cover example what is derivative x when square should it be s about quite applications buzzwords parameter optimization stochastic gradient descent autodiff backpropagation university tubingen all rights reserved content excluded from our creative commons license more information see https ocw ...

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