update

doc/LectureNotes/_build/html/week35.html

@@ -1611,7 +1611,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9963202597920238
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.99541140030306
 </pre></div>
 </div>
 </div>
@@ -1628,7 +1628,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.008550594684081035
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.009710035292693498
 </pre></div>
 </div>
 </div>
@@ -1643,31 +1643,31 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[1.09846636e-02 2.39074222e-02 4.94861749e-03 4.55640387e-02
- 1.31193395e-02 1.62470815e-03 2.46575843e-02 7.80638926e-02
- 2.19677483e-02 3.99901131e-02 2.68626148e-03 3.97731700e-02
- 4.79285683e-02 6.69363975e-02 1.02951554e-02 3.69826476e-02
- 6.29115152e-03 8.12289498e-02 3.11909685e-03 7.77144798e-04
- 8.15320771e-03 2.45720968e-02 5.12803365e-02 2.34378387e-02
- 2.33645986e-02 1.17796101e-02 2.80344720e-02 8.92995253e-02
- 1.24844778e-02 4.21582995e-02 1.89591553e-02 2.75357959e-02
- 2.44906236e-02 1.84031027e-02 2.09739330e-02 5.70421972e-03
- 2.55914617e-02 4.10427270e-02 6.55907387e-04 4.63734811e-03
- 8.42882114e-03 1.00851638e-04 5.19264104e-02 1.94917559e-05
- 6.96542994e-03 4.08924325e-03 4.08743287e-03 9.75853556e-03
- 4.04075841e-02 2.70473799e-02 1.72199579e-02 4.24501973e-02
- 7.08373167e-04 4.25201286e-02 5.69487891e-03 3.18217743e-03
- 1.85979474e-02 4.82250097e-02 8.96735081e-02 5.53952664e-02
- 5.51900727e-02 4.98842948e-03 1.30232456e-02 3.45220770e-02
- 4.12394241e-02 1.46400080e-02 2.27932015e-02 1.16054190e-03
- 1.53971260e-02 3.77533038e-02 8.53502096e-03 1.82404007e-02
- 1.86014003e-02 2.93792215e-02 3.66950317e-02 2.15456415e-02
- 2.21576737e-02 8.38730917e-03 2.98005862e-02 2.03932541e-03
- 4.60083846e-03 7.02160095e-03 5.76598941e-03 9.56807589e-03
- 3.49730046e-02 1.61899355e-02 1.47158029e-02 6.09404851e-04
- 5.14724135e-03 3.92916438e-02 2.88352503e-02 5.61520194e-02
- 4.33646268e-02 3.26506981e-03 1.51400374e-02 1.10235180e-02
- 7.63332882e-03 2.93229640e-02 6.85071429e-03 2.29148545e-03]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[3.79190435e-03 8.55070893e-03 3.86058560e-02 3.45643708e-02
+ 1.28500583e-02 5.10712245e-02 1.17764878e-02 1.13687085e-02
+ 1.74592114e-02 1.33939645e-03 2.27552531e-02 2.28284417e-02
+ 5.61536670e-02 6.48783370e-03 1.69356940e-02 7.00289406e-02
+ 2.45571233e-02 5.79106527e-02 7.31138734e-02 4.08448544e-02
+ 6.91811160e-03 4.97200305e-02 2.45268835e-02 1.97467734e-02
+ 5.60268567e-03 9.32784259e-04 7.08066746e-03 3.19910037e-03
+ 3.26521761e-02 1.36227629e-02 2.26896710e-02 3.55936475e-04
+ 2.49887949e-02 2.39417707e-05 2.39614347e-03 4.07789772e-02
+ 2.71075825e-02 6.82771329e-04 9.78018374e-03 8.87532289e-03
+ 2.42190899e-02 2.79827506e-02 1.34852463e-02 2.10441496e-02
+ 2.51850390e-02 4.86387759e-02 7.96257580e-03 2.40828861e-02
+ 3.60164827e-02 1.21503438e-01 4.62451626e-02 2.73628359e-02
+ 5.69981939e-03 6.30984244e-02 2.51054961e-03 3.86597540e-02
+ 3.69313663e-02 9.77836133e-03 5.43007002e-02 8.49862065e-03
+ 9.34037032e-03 2.08910476e-02 2.52606351e-02 5.07087395e-03
+ 4.11276576e-02 3.72904541e-02 2.13618516e-02 1.98880065e-02
+ 3.82975422e-02 1.18603611e-02 1.05236797e-02 4.00183687e-02
+ 6.56447742e-03 2.50173752e-02 3.42790075e-02 1.52902847e-02
+ 4.92169784e-02 3.50733669e-02 8.97665553e-03 4.56542321e-02
+ 3.57807896e-02 4.65359027e-03 1.02430520e-02 1.09480900e-03
+ 2.99948754e-02 1.60056803e-02 9.14266332e-02 2.21171751e-02
+ 2.21266166e-02 2.39548035e-02 1.67236038e-02 2.92337385e-02
+ 5.68040392e-02 3.33819480e-02 1.59981487e-02 3.13006658e-02
+ 3.20098424e-02 6.45146007e-03 3.82092612e-03 2.25724702e-02]
 </pre></div>
 </div>
 </div>
@@ -1736,15 +1736,15 @@ but now splitting the data into a training set and a test set.</p>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.90813514  0.65746064  4.09759873 -0.08945041  0.46023985]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.95079224  0.81447915  1.26265627  5.0771079  -2.04184782]
 Training R2
-0.9965048209369255
+0.9966080206050006
 Training MSE
-0.008089688732934681
+0.008334493024006613
 Test R2
-0.9963525271588923
+0.9954205504035075
 Test MSE
-0.0072559952738542694
+0.012501013881387798
 </pre></div>
 </div>
 </div>
@@ -3974,7 +3974,7 @@ the columns of <span class="math notranslate nohighlight">\(\boldsymbol{V}\)</sp
 <span class="math notranslate nohighlight">\(\boldsymbol{v}_i\)</span> are hierarchically ordered by how much correlation they
 encode from the columns of <span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span>.</p>
 <p>Note that these are also the eigenvectors and eigenvalues of the
-Hessian matrix.</p>
+Hessian matrix. Note also that the Hessian matrix we are discussing here is from a cost function defined by the  mean squared error only.</p>
 <p>If we now recall the definition of the covariance matrix (not using
 Bessel’s correction) we have</p>
 <div class="math notranslate nohighlight">

doc/LectureNotes/_build/jupyter_execute/week35.py

@@ -2392,7 +2392,7 @@ print(covariance_matrix)
 # encode from the columns of $\boldsymbol{X}$. 
 # 
 # Note that these are also the eigenvectors and eigenvalues of the
-# Hessian matrix.
+# Hessian matrix. Note also that the Hessian matrix we are discussing here is from a cost function defined by the  mean squared error only.
 # 
 # If we now recall the definition of the covariance matrix (not using
 # Bessel's correction) we have

doc/pub/week35/html/._week35-bs067.html

@@ -388,7 +388,7 @@ encode from the columns of \( \boldsymbol{X} \).
 </p>
 
 <p>Note that these are also the eigenvectors and eigenvalues of the
-Hessian matrix.
+Hessian matrix. Note also that the Hessian matrix we are discussing here is from a cost function defined by the  mean squared error only.
 </p>
 
 <p>If we now recall the definition of the covariance matrix (not using

doc/pub/week35/html/week35-reveal.html

@@ -3489,7 +3489,7 @@ encode from the columns of \( \boldsymbol{X} \).
 </p>
 
 <p>Note that these are also the eigenvectors and eigenvalues of the
-Hessian matrix.
+Hessian matrix. Note also that the Hessian matrix we are discussing here is from a cost function defined by the  mean squared error only.
 </p>
 
 <p>If we now recall the definition of the covariance matrix (not using

doc/pub/week35/html/week35-solarized.html

@@ -3306,7 +3306,7 @@ encode from the columns of \( \boldsymbol{X} \).
 </p>
 
 <p>Note that these are also the eigenvectors and eigenvalues of the
-Hessian matrix.
+Hessian matrix. Note also that the Hessian matrix we are discussing here is from a cost function defined by the  mean squared error only.
 </p>
 
 <p>If we now recall the definition of the covariance matrix (not using

doc/pub/week35/html/week35.html

@@ -3383,7 +3383,7 @@ encode from the columns of \( \boldsymbol{X} \).
 </p>
 
 <p>Note that these are also the eigenvectors and eigenvalues of the
-Hessian matrix.
+Hessian matrix. Note also that the Hessian matrix we are discussing here is from a cost function defined by the  mean squared error only.
 </p>
 
 <p>If we now recall the definition of the covariance matrix (not using

doc/src/week35/make.sh

@@ -49,6 +49,19 @@ system doconce split_html $html.html --method=split --pagination --nav_button=bo
 # IPython notebook
 system doconce format ipynb $name $opt
 
+# Ordinary plain LaTeX document
+rm -f *.aux  # important after beamer
+system doconce format pdflatex $name --minted_latex_style=trac --latex_admon=paragraph $opt
+system doconce ptex2tex $name envir=minted
+# Add special packages
+doconce subst "% Add user's preamble" "\g<1>\n\\usepackage{simplewick}" $name.tex
+doconce replace 'section{' 'section*{' $name.tex
+pdflatex -shell-escape $name
+pdflatex -shell-escape $name
+mv -f $name.pdf ${name}-minted.pdf
+cp $name.tex ${name}-plain-minted.tex
+
+
 
 # Publish
 dest=../../pub

doc/src/week35/week35.do.txt

@@ -2561,7 +2561,7 @@ encode from the columns of $\bm{X}$.
 
 
 Note that these are also the eigenvectors and eigenvalues of the
-Hessian matrix.
+Hessian matrix. Note also that the Hessian matrix we are discussing here is from a cost function defined by the  mean squared error only.
 
 If we now recall the definition of the covariance matrix (not using
 Bessel's correction) we have